Toward Building a Content based Video Recommendation System Based on Low-level Features
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This document proposes a content-based video recommendation system using low-level visual features to address the new item problem. It extracts features like average shot length, color variance, and motion from video content to classify videos into genres. An evaluation of the system used 120 videos across 4 genres and achieved a classification accuracy of 73.33%. The results showed this approach performed better than baselines in addressing the new item problem. Future work could analyze larger datasets and incorporate additional modalities like audio.
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Toward Building a Content based Video Recommendation System Based on Low-level Features
- 1. Toward Building a Content-based Video Recommendation System based on Low-level Features Yashar Deldjoo Mehdi Elahi Massimo Quadrana Paolo Cremonesi Corresponding journal ar8cle: Deldjoo, Yashar; Elahi, Mehdi; Cremonesi, Paolo; Garzo?o, Franca; Piazzolla, Pietro; Quadrana, Massimo; ",Content-Based Video Recommenda1on System Based on Stylis1c Visual Features, Journal on Data Seman.cs, 1-15, 2016, Springer
- 2. Outline • Introduction • New item problem • LL Feature base Recommendation • Evaluation and Results • Future work
- 3. tools that support users decision making by suggesting products that can be interesting to them. Examples of Recommender Systems: Recommender Systems:
- 4. Is typical done by predicting unknown ratings, by exploiting the content of items or/and ratings given by users. Recommendation: 3 1 2 5 2 3 4
- 5. when a new item is added to the catalogue and we don’t have information about it, e.g., no rating is available. New Item Problem: New Item 3 1 ? 2 5 2 ? 3 4 ?
- 6. Extreme New Item Problem We have absolutely no information about an item. Example: An unknown video content is uploaded by a unknown user and there is no metadata available. ? ? ? ? ? ? ? ? ? ?
- 7. How to make recommendation?
- 8. Video Content § There exist 3 main modalities in a video. Visual Audio Text
- 9. Video Content § There exist 3 main modalities in a video. § There exists many fearures in each modality. Visual Audio Text Visual Feaures Audio Feaures Textual Features
- 10. Video Content § There exist 3 main modalities in a video. § There exists many fearures in each modality. § Our focus Visual features Visual Audio Text Visual Feaures Audio Feaures Textual Features
- 11. Visual Features Visual Audio Text Feaures Audio Feaures Textual Features Visual Structure Content
- 12. Video Structure Scene: A number of shots that form a semantic unit. Shot: All frames within a single camera action. Frame: One static image from a series of static images constituting a video. Figure: Hierarchical decomposition and representation of video content, http://www.scholarpedia.org/article/Video_Content_Structuring
- 13. Example A: SHOT1
- 14. Example A: SHOT2
- 16. Example B: SHOT1
- 18. Shot Detection Figure: A schematic illustration of shot detection http://www.scholarpedia.org/article/Video_Content_Structuring
- 20. Average Shot Length Idea : Slower paced film (e.g. drama) have larger average shot length whereas action movies appear to have shorter average shot length.
- 21. Comparing Average Shot Length 0 2 4 6 8 10 12 14 Drama Action shot 1 shot 2 shot 3 shot 4 shot 5 shot 6 shot 7 shot 8 shot 9 shot 10 shot 11 shot 12 shot 13 shot 14 shot 15 shot 16 shot 17 shot 18
- 23. Color Variance figure Color Var (a) 0 (b) 0 (c) 0 (d) 0 (e) 0.25 (f) 17.8 (g) 1.4e+9
- 24. Horror Comedy
- 26. Shot Motion Figure 3: Optical flow of a sample image shown
- 29. Evaluation • 120 Videos • 4 Main Genres
- 32. Evaluation
- 33. Results N 1 2 3 4 5 6 7 8 9 10 recall 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LL HL - Genre LL+ HL - Genre Corresponding journal ar8cle: Deldjoo, Yashar; Elahi, Mehdi; Cremonesi, Paolo; Garzo?o, Franca; Piazzolla, Pietro; Quadrana, Massimo; ",Content-Based Video Recommenda1on System Based on Stylis1c Visual Features, Journal on Data Seman.cs, 1-15, 2016, Springer
- 34. Classification ClassificationAccuracy=73.33% a b c d Classified as 22 5 2 1 a = Action 1 27 2 0 b= Comedy 6 1 22 1 c= Drama 8 0 5 17 d=Horror
- 35. Conclusion • we propose a method to remedy the (extreme) New Item problem in video recommendation domain • we assume a more realistic scenario, i.e., an up- and-running video recommender with thousands of users • Result of our experiments shown that we have achieved excellent performance in comparison with considered baselines
- 36. Future Work • Further analysis with bigger datasets, in order to better understand the performance differences among the compared methods. • Investigation of the impact of different recommendation algorithms, such as those based on Bayesian, or SVD, on the performance of our method. • including additional sources of information, such as, audio features, in order to farther improve the quality of our content based recommendation method.