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Content-enriched Classifier for Web Video Classification
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Content-enriched Classifier for Web Video Classification


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Content-enriched Classifier for Web Video Classification

Content-enriched Classifier for Web Video Classification

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  • 1. Content-enriched Classifier C t t i h d Cl ifi for Web Video Classification o eb deo C ass cat o By Bin Cui & Ce Zhang Dept. Dept of CS Peking University Gao Cong School of CE, Nanyang Technological University, Singapore Presented by Ahmed Ibrahim SIGIR 2010
  • 2. Outline • Introduction • Current Approaches • Proposed Approach – Content -enriched Classifier – Content-enriched Similarity – CSE Classifier Algorithm • Experimental Results • Conclusions & Critique • Proposed approach extension
  • 3. Introduction • In video sharing services, the user services browses the web by categories. • Real time categorization plays a key roll for organizing, browsing, and retrieving online video. CS848 Winter 2011 3
  • 4. Web Video Processing Video Title User Description CS848 Winter 2011 4
  • 5. Web Video Classification Problems o Although text features and content features are complementary but utilizing content features in video classification stage is computationally expensive. o Text classification cannot use the rich information contained in video content. o Text description characteristics limits the classification performance of semantic similarity based on WordNet (and / or) term co-occurrence. co occurrence CS848 Winter 2011 5
  • 6. Current Approaches CS848 Winter 2011 6
  • 7. Proposed Approach 1- Content-Enriched Similarity: Using visual clues of web videos to obtain more reasonable semantic relations among words which called Content-Enriched Similarity (CES) between words. y( ) 2- Content-Enriched Nonlinear Classifier – At the training stage, a nonlinear SVM classifier is built to stage explore the semantic similarity between words using CES. – At the classification stage, this classifier classifies a new video g , using its text features (but not its content features). CS848 Winter 2011 7
  • 8. Proposed Approach (cont.) 3- Semantic kernels will be computed using the following formula: f l 4- Multi-Kernel Enhancement: Given several kernels created using different word pair-wise similarity matrices for multiple kernel optimization. CS848 Winter 2011 8
  • 9. Content enriched Content-enriched Classifier Training Data (Test Features) Classifier Content-Enriched Semantic Kernel Building Classifier Contentenriched word similarity Extract CES Finding the hyperplane in Content-Enriched Content Enriched Kernel Space Training Data (Test + Content Features) Applying Classifier Testing Data g (Test Features) CES: Content-Enriched Similarity CS848 Winter 2011 9
  • 10. Content Enriched Content-Enriched Similarity Generally, two words are similar if they appear in the y, y pp same cluster, within which the videos are similar in terms of content. Extract Visual Content Features Video Database (5149 videos) “K” clusters = 100 K-means Clustering Cl t i Project ‘tf’ into cluster space ‘VS’ video-cluster relation matrix CS848 Winter 2011 10
  • 11. CES Classifier Algorithm CS848 Winter 2011 11
  • 12. Experimental Results • Experimental Settings p g – Datasets: • Two real-life datasets are collected from ‘YouTube’ between Sept 23 & 24 of 2009, YT923 (5149 videos) & YT924(4447 videos). , ( ) ( ) • They categorized both datasets into 15 Categorize. – Preprocessing : Feature Extraction: • Text features are extracted from videos include (video titles and descriptions). • Words are stemmed using WordNet stemmer. • Stop words are manually removed. p y • The following visual content features (color, texture & edges) are extracted . CS848 Winter 2011 12
  • 13. Word Similarity Approach • The relation discovered by CSE are meaningful and agree with common sense. • The classification results reflect the superiority of proposed methods. CS848 Winter 2011 13
  • 14. Classification effectiveness • Classification Performance on different frameworks. • F-score: accuracy measure for classification which can be calculated using . • Macro-F: average of F-score for each category. • Micro-F: average of F-score for all decisions Micro F: F score decisions. CS848 Winter 2011 14
  • 15. Effectiveness Per Category The scores of content classifier have been excluded because their performance is much worse than the text. CS848 Winter 2011 15
  • 16. Performance on Multi-kernel Multi kernel • This table shows the results on classification effectiveness with multi-kernel solution multi kernel CS848 Winter 2011 16
  • 17. Conclusions • Novel Framework that exploits visual content and text features to facilitate online video categorization is presented. • Content-enriched Semantic Kernel which extracts word relationship by clustering the video with visual content feature is proposed. CS848 Winter 2011 17
  • 19. Camera Motion Model To enhance the presented approach, we will study the feasibility of using C i Camera M ti Motion M d l as a video content f t Model id t t feature on th the classification performance and efficiency using CC_WEB_VIDEO. CS848 Winter 2011 20
  • 20. Questions Thank You CS848 Winter 2011 21