Me12tt tub

Oct 9, 20120 likes198 views

The document evaluates different feature selection methods for bag-of-words approaches to video categorization. It finds that feature selection can improve results by filtering out non-informative terms. Metadata-based features like tags and descriptions generally outperform visual and audio features, but feature selection provides benefits across different feature types. The best performance comes from combining multiple feature types with transformation and selection techniques.

Technology

Feature Selection Methods for Bag-
of-(visual)-Words Approaches
Schmiedeke, Kelm and Sikora
Communication Systems Group
Technische Universität Berlin

4 October, 2012

Motivation 2

sports

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Lessons from last year 3

Features derived from metadata (esp. tags)
outperform visual and ASR ones
• Metadata: Naive Bayes (non translated)
• Visual feat.: SVM (avg. pooled histograms)
• ASR transcripts: kNN (JSD)

Uploader mainly contribute to a single category

Schmiedeke: “Feature Selection Methods for BoW Approaches”

This year‘s question 4

Does feature selection improve results achieved
with BoW model?

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Feature Selection/ Transformation 5

Mutual information:

Term Frequency:

PCA (Eigenvalue decomposition):

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Feature Selection 6

Concepts for terms selection:

Top terms for religion: Top terms for politics: Top terms for health:
bibl (0.0897) lunch (0.1200) jama (0.0495)
jesu (0.0797) obama (0.1113) health (0.0378)
god (0.0796) polit (0.0982) report (0.0357)
unleaven(0.0782) grittv (0.0881) harta (0.0227)
eeli (0.0782) flander (0.0861) exceric (0.0211)
davideel(0.0781) laura (0.0855) yoga (0.0203)
ministri(0.0780) economi(0.0747) study (0.0192)

… … …

daytripp (0.0) sonnet (0.0) ilsr (0.0)
adagio (0.0) screenplai (0.0) resystem (0.0)
acustica (0.0) acustica (0.0) acustica (0.0)

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Feature Selection 7

Top-k-Union:

Top terms for religion: Top terms for politics: Top terms for health:
bibl (0.0897) lunch (0.1200) jama (0.0495)
jesu (0.0797) obama (0.1113) health (0.0378)
god (0.0796) polit (0.0982) report (0.0357)
unleaven(0.0782) grittv (0.0881) harta (0.0227)
eeli (0.0782) flander (0.0861) exceric (0.0211)
davideel(0.0781) laura (0.0855) yoga (0.0203)
misistri(0.0780) economi(0.0747) study (0.0192)

… … …

daytripp (0.0) sonnet (0.0) ilsr (0.0)
adagio (0.0) screenplai (0.0) resystem (0.0)
acustica (0.0) acustica (0.0) acustica (0.0)

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Feature Selection 8

Top-k:

Top terms for religion: Top terms for politics: Top terms for health:
bibl (0.0897) lunch (0.1200) jama (0.0495)
jesu (0.0797) obama (0.1113) health (0.0378)
god (0.0796) polit (0.0982) report (0.0357)
unleaven(0.0782) grittv (0.0881) harta (0.0227)
eeli (0.0782) flander (0.0861) exceric (0.0211)
davideel(0.0781) laura (0.0855) yoga (0.0203)
misistri(0.0780) economi(0.0747) study (0.0192)

… … …

daytripp (0.0) sonnet (0.0) ilsr (0.0)
adagio (0.0) screenplai (0.0) resystem (0.0)
acustica (0.0) acustica (0.0) acustica (0.0)

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Feature Selection 9

Union>th:

Top terms for religion: Top terms for politics: Top terms for health:
bibl (0.0897) lunch (0.1200) jama (0.0495)
jesu (0.0797) obama (0.1113) health (0.0378)
god (0.0796) polit (0.0982) report (0.0357)
unleaven(0.0782) grittv (0.0881) harta (0.0227)
eeli (0.0782) flander (0.0861) exceric (0.0211)
davideel(0.0781) laura (0.0855) yoga (0.0203)
misistri(0.0780) economi(0.0747) study (0.0192)

… … …

daytripp (0.0) sonnet (0.0) ilsr (0.0)
adagio (0.0) screenplai (0.0) resystem (0.0)
acustica (0.0) acustica (0.0) acustica (0.0)
0.0002 0.0002 0.0001

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Feature Selection 10

Intersection>Th:

Top terms for religion: Top terms for politics: Top terms for health:
bibl (0.0897) lunch (0.1200) jama (0.0495)
jesu (0.0797) obama (0.1113) health (0.0378)
god (0.0796) polit (0.0982) report (0.0357)
… … …
web appl gossip
python googl interview
xbox teen iphon
big music san
expo tv texa
… … …
daytripp (0.0) sonnet (0.0) ilsr (0.0)
adagio (0.0) screenplai (0.0) resystem (0.0)
acustica (0.0) acustica (0.0) acustica (0.0)
0.0002 0.0002 0.0001

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Official runs 11

Bag of clustered SURF features transformed
using PCA
• Result does not benefit from transformation

official run without FS/FT
mAP 0.2301 0.2309
CA 41.63 % 41.71 %

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Official runs 12

Bag of filtered ASR transcripts terms (Union>Th)
• Result does benefit from selection

official run without FS/FT
mAP 0.1035 0.0522
CA 32.53 % 26.54 %

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Official runs 13

Bag of clustered SURF features filtered using MI
and intersection>th strategy
• Result does slightly benefit from selection

official run without FS/FT
mAP 0.2259 0.2221
CA 40.80 % 40.78 %

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Official runs 14

Bag of filtered terms derived from tags, title and
descriptions (Union>Th)
• Result does benefit from selection

official run without FS/FT
mAP 0.5225 0.4146
CA 58.18 % 55.70 %

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Official runs 15

Bag of clustered SURF features transformed
using PCA and decision fusion using uploader
• Result does benefit from transformation

official run without FS/FT
mAP 0.3304 0.2988
CA 52.14 % 49.19 %

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Conclusion & Future Work 16

FS showed potential for improving the results

Choice of using MI or TF is not critical, both
methods achieve roughly same results
• Metadata (mAP) : MI12004 (0.5277) vs. TF14976 (0.5275)

Investigation in different scaling schemes (NB)

Use of class-independent selection score (MI)

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Backup 17

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Backup 18

Schmiedeke: “Feature Selection Methods for BoW Approaches”

Extracting visual features 19

SURF are extracted from each key frame
• At keypoints and at a regular grid

Vocabulary is built using hierarchical clustering
on SURF features of development set
• 4096/8196 codewords

Term vector for a single video is obtained by bin-
wise pooling of each key frames’ term vector
• avg

Schmiedeke: “Feature Selection Methods for BoW Approaches”

MediaEval 2012: Tagging Task 20

Question: What is the videos’ blip.tv category?
Blip.tv database (cc): ~ 3300 h
• 5288 training videos
• 9550 test videos
Official evaluation measurement is Mean
Average Precision (mAP)
Workshop will be held 4-5 October 2012 in Pisa,
Italy

Schmiedeke: “Feature Selection Methods for BoW Approaches”

The document evaluates different feature selection methods for bag-of-words approaches. It finds that feature selection can improve results achieved with bag-of-words models, depending on the features and selection method used. When applied to clustered SURF features transformed with PCA, filtered ASR transcripts terms, and metadata tags, the feature selection methods led to improved mean average precision and classification accuracy compared to using the features without selection. The choice of mutual information or term frequency for selection was not critical, as both achieved similar results.

Unit 2 boolean algebra and logic gatesAmrutaMehata

��

This document provides an introduction to Boolean algebra, which describes the behavior of digital circuits. It defines key concepts such as binary values, complement/NOT operations, AND and OR operations. It also outlines several important postulates and theorems of Boolean algebra, including identities, commutativity, absorption, De Morgan's theorems, and Shannon's expansion theorem. The document is intended to teach the basic foundations of Boolean algebra used in digital circuit design and logic gate optimization.

An introduction to variable and feature selectionMarco Meoni

��

The document discusses variable and feature selection techniques in machine learning, outlining definitions, goals, and methods including filters, wrappers, and embedded techniques. It emphasizes the importance of selecting relevant features to enhance classifier performance, reduce training time, and limit overfitting. Additionally, it addresses challenges in feature extraction, validation methods, and advanced topics such as multi-class problems and causal inference.

Image retrieval based on feature selection methodeSAT Publishing House

��

This paper presents a content-based image retrieval (CBIR) system that utilizes texture features extracted by the gray level co-occurrence matrix (GLCM) and optimized through a genetic algorithm for feature selection. The method aims to improve retrieval accuracy and reduce computational demands, showing enhanced precision and recall in retrieving relevant images from a coral database. The results indicate that the feature selection method significantly increases retrieval performance compared to basic feature extraction alone.

Exploratory Analysis of Feature Selection Techniques in Medical Image ProcessingAssociation of Scientists, Developers and Faculties

��

This paper explores feature selection techniques used in medical image processing, emphasizing their importance in data mining and knowledge discovery by eliminating redundant features while retaining essential information. It outlines the stages of medical image processing, including image capture, enhancement, segmentation, and feature extraction, and discusses various feature selection methods utilized to improve classifier performance and reduce computational costs. The paper suggests the need for further research to combine existing selection methods for enhanced real-time performance.

3. introduction to text miningLokesh Ramaswamy

��

This document provides an introduction to text mining, including defining key concepts such as structured vs. unstructured data, why text mining is useful, and some common challenges. It also outlines important text mining techniques like pre-processing text through normalization, tokenization, stemming, and removing stop words to prepare text for analysis. Text mining methods can be used for applications such as sentiment analysis, predicting markets or customer churn.

Using support vector machine with a hybrid feature selection method to the st...lolokikipipi

��

This document discusses using a support vector machine (SVM) with a hybrid feature selection method to predict stock trends. It proposes using F-score filtering followed by a wrapper method called Supported Sequential Forward Search (SSFS) to select optimal features for the SVM. An experiment applies this approach to NASDAQ index data, reducing 30 features to 17 using F_SSFS and achieving a classification accuracy of 81.7% with the SVM, outperforming a backpropagation neural network. The hybrid approach helps address overfitting issues while improving the SVM's prediction performance.

Text miningAli A Jalil

��

This document discusses text mining and provides an outline of the topic. It defines text mining as the analysis of natural language text data and explains why it is useful given the large amount of unstructured data. The document then describes the basic text mining process, which includes steps like filtering, segmentation, stemming, eliminating excessive words, and clustering. Several applications of text mining are mentioned like call centers, anti-spam, and market intelligence. Challenges of text mining like dealing with unstructured data and large collections of documents are also outlined.

Support Vector machineAnandha L Ranganathan

��

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression analysis. It works by finding a hyperplane in an N-dimensional space that distinctly classifies the data points. SVM selects the hyperplane that has the largest distance to the nearest training data points of any class, since larger the margin lower the generalization error of the classifier. SVM can efficiently perform nonlinear classification by implicitly mapping their inputs into high-dimensional feature spaces.

Introduction to Text MiningMinha Hwang

��

The class outline covers introduction to unstructured data analysis, word-level analysis using vector space model and TF-IDF, beyond word-level analysis using natural language processing, and a text mining demonstration in R mining Twitter data. The document provides background on text mining, defines what text mining is and its tasks. It discusses features of text data and methods for acquiring texts. It also covers word-level analysis methods like vector space model and TF-IDF, and applications. It discusses limitations of word-level analysis and how natural language processing can help. Finally, it demonstrates Twitter mining in R.

Support Vector Machine without tearsAnkit Sharma

��

This document provides an overview of support vector machines (SVMs), including their basic concepts, formulations, and applications. SVMs are supervised learning models that analyze data, recognize patterns, and are used for classification and regression. The document explains key SVM properties, the concept of finding an optimal hyperplane for classification, soft margin SVMs, dual formulations, kernel methods, and how SVMs can be used for tasks beyond binary classification like regression, anomaly detection, and clustering.

Support Vector Machinesnextlib

��

This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.

Support Vector MachineShao-Chuan Wang

��

This document provides an overview of support vector machines (SVMs). It discusses how SVMs can be used to perform classification tasks by finding optimal separating hyperplanes that maximize the margin between different classes. The document outlines how SVMs solve an optimization problem to find these optimal hyperplanes using techniques like Lagrange duality, kernels, and soft margins. It also covers model selection methods like cross-validation and discusses extensions of SVMs to multi-class classification problems.

Feature Selection in Machine LearningUpekha Vandebona

��

The document discusses feature selection techniques applied to various datasets including the Iris and Abalone datasets, emphasizing the importance of identifying relevant input variables to enhance neural network performance. It outlines methods such as forward selection, backward elimination, and genetic algorithms while highlighting the curse of dimensionality and the need for model generalization. A variety of findings and conclusions are drawn regarding which features are significant in predicting outcomes, noting that simplification can improve model efficacy despite potential performance costs.

Textmining IntroductionDatamining Tools

��

Text mining refers to extracting knowledge from unstructured text data. It is needed because most biological knowledge exists in unstructured research papers, making it difficult for scientists to manually analyze large amounts of text. Challenges include dealing with noisy, unstructured data and complex relationships between concepts. The text mining process involves preprocessing text through steps like tokenization, feature selection, and parsing to extract meaningful features before analysis can be done through classification, clustering, or other techniques. Potential applications are wide-ranging across domains like customer profiling, trend analysis, and web search.

Support Vector Machines for ClassificationPrakash Pimpale

��

The document provides an overview of support vector machines (SVM), focusing on their theoretical foundation, implementation, and effectiveness for classification and regression tasks. It discusses the Vapnik-Chervonenkis theory, the principles of maximizing the margin between decision boundaries, and various kernel functions used for handling non-linearly separable data. Additionally, it highlights some readily available SVM implementations and common issues encountered when applying these techniques.

Feature selectionDong Guo

��

This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.

Feature selection concepts and methodsReza Ramezani

��

This document discusses feature selection concepts and methods. It defines features as attributes that determine which class an instance belongs to. Feature selection aims to select a relevant subset of features by removing irrelevant, redundant and unnecessary data. This improves learning accuracy, model performance and interpretability. The document categorizes feature selection algorithms as filter, wrapper or embedded methods based on how they evaluate feature subsets. It also discusses concepts like feature relevance, search strategies, successor generation and evaluation measures used in feature selection algorithms.

A Review on Feature Selection Methods For Classification TasksEditor IJCATR

��

The document provides a review of feature selection methods applied to medical datasets, highlighting the challenges of selecting an optimal subset of relevant features without increasing model complexity. It categorizes feature selection techniques into filter, wrapper, embedded, and hybrid methods, discussing their advantages and limitations. The review emphasizes the ongoing need for effective methods that enhance classification accuracy while managing dimensionality in high-dimensional data.

An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka

��

The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.

Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand

��

The Future of Product Management in AI ERA.pdfAlyona Owens

��

Hi, I’m Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around. Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as I’ve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs… We’ll talk about it shortly. But before that, I want to take you back to 1997. One of my favorite movies is “Fifth Element”. It wowed me with futuristic predictions when I was a kid and I’m impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves: Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink). Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully). On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldn’t be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future. So, we as humans, when we don’t know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didn’t steal jobs—they transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.

" How to survive with 1 billion vectors and not sell a kidney: our low-cost c...Fwdays

��

Let's talk about our history. How we started the project with a small vector database of less than 2 million records. Later, we received a request for +100 million records, then another +100... And so gradually we reached almost 1 billion. Standard tools were quickly running out of steam - we were running into performance, index size, and very limited resources. After a long series of trials and errors, we built our own low-cost cluster, which today stably processes thousands of queries to more than 1B vectors.

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

��

Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!

Daily Lesson Log MATATAG ICT TEchnology 8LOIDAALMAZAN3

��

10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash

��

Raman Bhaumik - Passionate Tech EnthusiastRaman Bhaumik

��

Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdfPriyanka Aash

��

Hyderabad MuleSoft In-Person Meetup (June 21, 2025) �ݺ�ߣsRavi Tamada

��

Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software

��

Managing airport and airspace data is no small task, especially when you’re expected to deliver it in AIXM format without spending a fortune on specialized tools. But what if there was a smarter, more affordable way? Join us for a behind-the-scenes look at how Sweco partnered with Swedavia, the Swedish airport operator, to solve this challenge using FME and Esri. Learn how they built automated workflows to manage periodic updates, merge airspace data, and support data extracts – all while meeting strict government reporting requirements to the Civil Aviation Administration of Sweden. Even better? Swedavia built custom services and applications that use the FME Flow REST API to trigger jobs and retrieve results – streamlining tasks like securing the quality of new surveyor data, creating permdelta and baseline representations in the AIS schema, and generating AIXM extracts from their AIS data. To conclude, FME expert Dean Hintz will walk through a GeoBorders reading workflow and highlight recent enhancements to FME’s AIXM (Aeronautical Information Exchange Model) processing and interpretation capabilities. Discover how airports like Swedavia are harnessing the power of FME to simplify aviation data management, and how you can too.

More Related Content

Viewers also liked (12)

Support Vector machineAnandha L Ranganathan

��

Introduction to Text MiningMinha Hwang

��

Support Vector Machine without tearsAnkit Sharma

��

Support Vector Machinesnextlib

��

Support Vector MachineShao-Chuan Wang

��

Feature Selection in Machine LearningUpekha Vandebona

��

Textmining IntroductionDatamining Tools

��

Support Vector Machines for ClassificationPrakash Pimpale

��

Feature selectionDong Guo

��

Feature selection concepts and methodsReza Ramezani

��

A Review on Feature Selection Methods For Classification TasksEditor IJCATR

��

An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka

��

Support Vector machineAnandha L Ranganathan

��

Introduction to Text MiningMinha Hwang

��

Support Vector Machine without tearsAnkit Sharma

��

Support Vector Machinesnextlib

��

Support Vector MachineShao-Chuan Wang

��

Feature Selection in Machine LearningUpekha Vandebona

��

Textmining IntroductionDatamining Tools

��

Support Vector Machines for ClassificationPrakash Pimpale

��

Feature selectionDong Guo

��

Feature selection concepts and methodsReza Ramezani

��

A Review on Feature Selection Methods For Classification TasksEditor IJCATR

��

An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka

��

Recently uploaded (20)

Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand

��

The Future of Product Management in AI ERA.pdfAlyona Owens

��

" How to survive with 1 billion vectors and not sell a kidney: our low-cost c...Fwdays

��

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

��

Daily Lesson Log MATATAG ICT TEchnology 8LOIDAALMAZAN3

��

10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash

��

Raman Bhaumik - Passionate Tech EnthusiastRaman Bhaumik

��

Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdfPriyanka Aash

��

Hyderabad MuleSoft In-Person Meetup (June 21, 2025) �ݺ�ߣsRavi Tamada

��

Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software

��

cnc-processing-centers-centateq-p-110-en.pdfAmirStern2

��

מרכז עיבודים תעשייתי בעל 3/4/5 צירים, עד 22 החלפות כלים עם כל אפשרויות העיבוד הדרושות.��בעל שטח עבודה גדול ומחשב נוח וקל להפעלה בשפה העברית/רוסית/אנגלית/ספרדית/ערבית ועוד.. מסוגל לבצע פעולות עיבוד שונות המתאימות לענפים שונים: קידוח אנכי, אופקי, ניסור, וכרסום אנכי.

UserCon Belgium: Honey, VMware increased my billstijn40

��

VMware’s pricing changes have forced organizations to rethink their datacenter cost management strategies. While FinOps is commonly associated with cloud environments, the FinOps Foundation has recently expanded its framework to include Scopes—and Datacenter is now officially part of the equation. In this session, we’ll map the FinOps Framework to a VMware-based datacenter, focusing on cost visibility, optimization, and automation. You’ll learn how to track costs more effectively, rightsize workloads, optimize licensing, and drive efficiency—all without migrating to the cloud. We’ll also explore how to align IT teams, finance, and leadership around cost-aware decision-making for on-prem environments. If your VMware bill keeps increasing and you need a new approach to cost management, this session is for you!

Python Conference Singapore - 19 Jun 2025ninefyi

��

“MPU+: A Transformative Solution for Next-Gen AI at the Edge,” a Presentation...Edge AI and Vision Alliance

��

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/mpu-a-transformative-solution-for-next-gen-ai-at-the-edge-a-presentation-from-fotonation/ Petronel Bigioi, CEO of FotoNation, presents the “MPU+: A Transformative Solution for Next-Gen AI at the Edge” tutorial at the May 2025 Embedded Vision Summit. In this talk, Bigioi introduces MPU+, a novel programmable, customizable low-power platform for real-time, localized intelligence at the edge. The platform includes an AI-augmented image signal processor that enables leading image and video quality. In addition, it integrates ultra-low-power object and motion detection capabilities to enable always-on computer vision. A programmable neural processor provides flexibility to efficiently implement new neural networks. And additional specialized engines facilitate image stabilization and audio enhancements.

"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...Fwdays

��

You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante

��

We live in an ever evolving landscape for cyber threats creating security risk for your production systems. Mitigating these risks requires participation throughout all stages from development through production delivery - and by every role including architects, developers QA and DevOps engineers, product owners and leadership. No one is excused! This session will cover examples of common mistakes or missed opportunities that can lead to vulnerabilities in production - and ways to do better throughout the development lifecycle.

Techniques for Automatic Device Identification and Network Assignment.pdfPriyanka Aash

��

"Database isolation: how we deal with hundreds of direct connections to the d...Fwdays

��

What can go wrong if you allow each service to access the database directly? In a startup, this seems like a quick and easy solution, but as the system scales, problems appear that no one could have guessed. In my talk, I'll share Solidgate's experience in transforming its architecture: from the chaos of direct connections to a service-based data access model. I will talk about the transition stages, bottlenecks, and how isolation affected infrastructure support. I will honestly show what worked and what didn't. In short, we will analyze the controversy of this talk.

Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...All Things Open

��

Presented at All Things Open RTP Meetup Presented by William Hill - Developer Advocate, NVIDIA Title: Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines of Code and the Concepts Behind the Code Abstract: In this talk we will demonstrate building a working data science AI agent in 10 lines of basic Python code in a Colab notebook. Our AI Agent will perform LLM prompt-driven visual analysis using open-source libraries. In this session we will show how to develop an AI Agent using GPUs through NVIDIA’s developer program and Google Colab notebooks. After coding our AI Agent, we will break down the 10 lines of code. We will show the key components and open source library integrations that enable the agent's functionality, focusing on practical implementation details and then the theoretical concepts. The presentation concludes with a survey of current LLM technologies and the latest trends in developing AI applications for enthusiasts and enterprises

The Growing Value and Application of FME & GenAISafe Software

��

With the cost of using Generative AI services dropping exponentially and the array of available models continually expanding, integrating AI into FME workflows has become inexpensive, accessible and effective. This presentation explores how GenAI within FME can cost-effectively transform data workflows by automating data extraction, validation, classification and augmentation tasks. We’ll discuss how FME’s no-code flexibility enables users to combine Generative AI and Computer Vision tools that create efficient workflows tailored to specific challenges. Using recent practical examples, we’ll demonstrate how these integrations can simplify complex tasks, save time and enhance data quality.

Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand

��

The Future of Product Management in AI ERA.pdfAlyona Owens

��

" How to survive with 1 billion vectors and not sell a kidney: our low-cost c...Fwdays

��

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

��

Daily Lesson Log MATATAG ICT TEchnology 8LOIDAALMAZAN3

��

10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash

��

Raman Bhaumik - Passionate Tech EnthusiastRaman Bhaumik

��

Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdfPriyanka Aash

��

Hyderabad MuleSoft In-Person Meetup (June 21, 2025) �ݺ�ߣsRavi Tamada

��

Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software

��

cnc-processing-centers-centateq-p-110-en.pdfAmirStern2

��

UserCon Belgium: Honey, VMware increased my billstijn40

��

Python Conference Singapore - 19 Jun 2025ninefyi

��

“MPU+: A Transformative Solution for Next-Gen AI at the Edge,” a Presentation...Edge AI and Vision Alliance

��

"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...Fwdays

��

You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante

��

Techniques for Automatic Device Identification and Network Assignment.pdfPriyanka Aash

��

"Database isolation: how we deal with hundreds of direct connections to the d...Fwdays

��

Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...All Things Open

��

The Growing Value and Application of FME & GenAISafe Software

��

Me12tt tub

1. Feature Selection Methods for Bag- of-(visual)-Words Approaches Schmiedeke, Kelm and Sikora Communication Systems Group Technische Universität Berlin 4 October, 2012

2. Motivation 2 sports Schmiedeke: “Feature Selection Methods for BoW Approaches”

3. Lessons from last year 3 Features derived from metadata (esp. tags) outperform visual and ASR ones • Metadata: Naive Bayes (non translated) • Visual feat.: SVM (avg. pooled histograms) • ASR transcripts: kNN (JSD) Uploader mainly contribute to a single category Schmiedeke: “Feature Selection Methods for BoW Approaches”

4. This year‘s question 4 Does feature selection improve results achieved with BoW model? Schmiedeke: “Feature Selection Methods for BoW Approaches”

5. Feature Selection/ Transformation 5 Mutual information: Term Frequency: PCA (Eigenvalue decomposition): Schmiedeke: “Feature Selection Methods for BoW Approaches”

6. Feature Selection 6 Concepts for terms selection: Top terms for religion: Top terms for politics: Top terms for health: bibl (0.0897) lunch (0.1200) jama (0.0495) jesu (0.0797) obama (0.1113) health (0.0378) god (0.0796) polit (0.0982) report (0.0357) unleaven(0.0782) grittv (0.0881) harta (0.0227) eeli (0.0782) flander (0.0861) exceric (0.0211) davideel(0.0781) laura (0.0855) yoga (0.0203) ministri(0.0780) economi(0.0747) study (0.0192) … … … daytripp (0.0) sonnet (0.0) ilsr (0.0) adagio (0.0) screenplai (0.0) resystem (0.0) acustica (0.0) acustica (0.0) acustica (0.0) Schmiedeke: “Feature Selection Methods for BoW Approaches”

7. Feature Selection 7 Top-k-Union: Top terms for religion: Top terms for politics: Top terms for health: bibl (0.0897) lunch (0.1200) jama (0.0495) jesu (0.0797) obama (0.1113) health (0.0378) god (0.0796) polit (0.0982) report (0.0357) unleaven(0.0782) grittv (0.0881) harta (0.0227) eeli (0.0782) flander (0.0861) exceric (0.0211) davideel(0.0781) laura (0.0855) yoga (0.0203) misistri(0.0780) economi(0.0747) study (0.0192) … … … daytripp (0.0) sonnet (0.0) ilsr (0.0) adagio (0.0) screenplai (0.0) resystem (0.0) acustica (0.0) acustica (0.0) acustica (0.0) Schmiedeke: “Feature Selection Methods for BoW Approaches”

8. Feature Selection 8 Top-k: Top terms for religion: Top terms for politics: Top terms for health: bibl (0.0897) lunch (0.1200) jama (0.0495) jesu (0.0797) obama (0.1113) health (0.0378) god (0.0796) polit (0.0982) report (0.0357) unleaven(0.0782) grittv (0.0881) harta (0.0227) eeli (0.0782) flander (0.0861) exceric (0.0211) davideel(0.0781) laura (0.0855) yoga (0.0203) misistri(0.0780) economi(0.0747) study (0.0192) … … … daytripp (0.0) sonnet (0.0) ilsr (0.0) adagio (0.0) screenplai (0.0) resystem (0.0) acustica (0.0) acustica (0.0) acustica (0.0) Schmiedeke: “Feature Selection Methods for BoW Approaches”

9. Feature Selection 9 Union>th: Top terms for religion: Top terms for politics: Top terms for health: bibl (0.0897) lunch (0.1200) jama (0.0495) jesu (0.0797) obama (0.1113) health (0.0378) god (0.0796) polit (0.0982) report (0.0357) unleaven(0.0782) grittv (0.0881) harta (0.0227) eeli (0.0782) flander (0.0861) exceric (0.0211) davideel(0.0781) laura (0.0855) yoga (0.0203) misistri(0.0780) economi(0.0747) study (0.0192) … … … daytripp (0.0) sonnet (0.0) ilsr (0.0) adagio (0.0) screenplai (0.0) resystem (0.0) acustica (0.0) acustica (0.0) acustica (0.0) 0.0002 0.0002 0.0001 Schmiedeke: “Feature Selection Methods for BoW Approaches”

10. Feature Selection 10 Intersection>Th: Top terms for religion: Top terms for politics: Top terms for health: bibl (0.0897) lunch (0.1200) jama (0.0495) jesu (0.0797) obama (0.1113) health (0.0378) god (0.0796) polit (0.0982) report (0.0357) … … … web appl gossip python googl interview xbox teen iphon big music san expo tv texa … … … daytripp (0.0) sonnet (0.0) ilsr (0.0) adagio (0.0) screenplai (0.0) resystem (0.0) acustica (0.0) acustica (0.0) acustica (0.0) 0.0002 0.0002 0.0001 Schmiedeke: “Feature Selection Methods for BoW Approaches”

11. Official runs 11 Bag of clustered SURF features transformed using PCA • Result does not benefit from transformation official run without FS/FT mAP 0.2301 0.2309 CA 41.63 % 41.71 % Schmiedeke: “Feature Selection Methods for BoW Approaches”

12. Official runs 12 Bag of filtered ASR transcripts terms (Union>Th) • Result does benefit from selection official run without FS/FT mAP 0.1035 0.0522 CA 32.53 % 26.54 % Schmiedeke: “Feature Selection Methods for BoW Approaches”

13. Official runs 13 Bag of clustered SURF features filtered using MI and intersection>th strategy • Result does slightly benefit from selection official run without FS/FT mAP 0.2259 0.2221 CA 40.80 % 40.78 % Schmiedeke: “Feature Selection Methods for BoW Approaches”

14. Official runs 14 Bag of filtered terms derived from tags, title and descriptions (Union>Th) • Result does benefit from selection official run without FS/FT mAP 0.5225 0.4146 CA 58.18 % 55.70 % Schmiedeke: “Feature Selection Methods for BoW Approaches”

15. Official runs 15 Bag of clustered SURF features transformed using PCA and decision fusion using uploader • Result does benefit from transformation official run without FS/FT mAP 0.3304 0.2988 CA 52.14 % 49.19 % Schmiedeke: “Feature Selection Methods for BoW Approaches”

16. Conclusion & Future Work 16 FS showed potential for improving the results Choice of using MI or TF is not critical, both methods achieve roughly same results • Metadata (mAP) : MI12004 (0.5277) vs. TF14976 (0.5275) Investigation in different scaling schemes (NB) Use of class-independent selection score (MI) Schmiedeke: “Feature Selection Methods for BoW Approaches”

17. Backup 17 Schmiedeke: “Feature Selection Methods for BoW Approaches”

18. Backup 18 Schmiedeke: “Feature Selection Methods for BoW Approaches”

19. Extracting visual features 19 SURF are extracted from each key frame • At keypoints and at a regular grid Vocabulary is built using hierarchical clustering on SURF features of development set • 4096/8196 codewords Term vector for a single video is obtained by bin- wise pooling of each key frames’ term vector • avg Schmiedeke: “Feature Selection Methods for BoW Approaches”

20. MediaEval 2012: Tagging Task 20 Question: What is the videos’ blip.tv category? Blip.tv database (cc): ~ 3300 h • 5288 training videos • 9550 test videos Official evaluation measurement is Mean Average Precision (mAP) Workshop will be held 4-5 October 2012 in Pisa, Italy Schmiedeke: “Feature Selection Methods for BoW Approaches”

�ݺ�ߣ

Me12tt tub

Recommended

More Related Content

Viewers also liked (12)

Recently uploaded (20)

Me12tt tub