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.
TUB @ MediaEval 2012 Tagging Task: Feature Selection Methods for Bag-of-(visu...MediaEval2012
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
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This paper analyses features selection method used in medical image processing. How image is selected by using diverse sort of method similarly: screening, scanning and selecting. We discussed on feature selection procedure which is extensively used for data mining and knowledge discovery and it carryout elimination of redundant features, concomitantly retaining the fundamental bigoted information, feature selection implies less data transmission and efficient data mining. It accentuates the need for further research in the field of pattern recognition that can effectively determine the situation with captured portion of human body.
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.
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.
TUB @ MediaEval 2012 Tagging Task: Feature Selection Methods for Bag-of-(visu...MediaEval2012
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
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This paper analyses features selection method used in medical image processing. How image is selected by using diverse sort of method similarly: screening, scanning and selecting. We discussed on feature selection procedure which is extensively used for data mining and knowledge discovery and it carryout elimination of redundant features, concomitantly retaining the fundamental bigoted information, feature selection implies less data transmission and efficient data mining. It accentuates the need for further research in the field of pattern recognition that can effectively determine the situation with captured portion of human body.
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.
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 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.
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.
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.
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.
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.
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.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
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.
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
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
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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.
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.
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.
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.
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.
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.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
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.
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
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
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.
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https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
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Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
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Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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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
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”