今日から始める人工知能 × 機械学習 Meetup ライトニングトーク1
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今日から始める人工知能 × 機械学習 Meetup ライトニングトーク1
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今日から始める人工知能 × 機械学習 Meetup ライトニングトーク1
- 1. Large-scale Semantic Visual Search NGUYEN ANH TUAN tuannguyen.research@gmail.com 2016/07/17
- 2. About me • 東京大学 情報理工学系研究科 修士2年生 • テーマ：Object Retrieval，情 報検索等 • 趣味：水泳，囲碁 • ブログ： https://imsmarxen68.tumblr.co m/ NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 3. A picture is worth a thousand words
- 4. Outline • Semantic Visual Search • A visual search framework Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html Feature extraction Feature aggregation Feature matching Re-ranking Preliminary results Final results NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 5. Visual search Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html Image credits: http://google.com NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 6. What’s the problem? • Semantic difficulties: fine-grained differences Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 7. But for search problem? Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html Query Database NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 8. But for search problem? Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html Query Database 0.1 0.5 0.2Ranking problem with a variation of fine-grained changes NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 9. But for search problem? Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html Query Database 0.1 0.5 0.2Find visual representations to capture all fine-grained local information in images NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 11. Robust feature extraction • Robust to – Scale changes – Rotation and affine changes – Blur, sharpening, … Feature extraction Feature aggregation Feature matching Re-ranking Preliminary results Final results Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html A picture is worth a thousand words NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 12. Statistical kernels • Bag-of-Features (BoF) • Fisher kernel (GMM) [1] • VLAD (K-means) [2] Image credits: http://www.mathworks.com/matlabcentral/ Feature extraction Feature aggregation Feature matching Re-ranking Preliminary results Final results [1] F. Perronnin, C. Dance, “Fisher Kernels on Visual Vocabularies for Image Categorization,” in Proc. CVPR, IEEE, 2007 [2] H. Jegou, F. Perronnin, M. Douze, J. Sanchez, P. Perez, C. Schmid, “Aggregating Local Image Descriptors into Compact Codes,” IEEE Trans. Pattern Anal. Mach. Intell. 34 (2012) 1704–1716. NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 13. Statistical kernels Feature extraction Feature aggregation Feature matching Re-ranking Preliminary results Final results NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 14. Image matching = Feature matching • Feature matching→Nearest Neighbor Search – Inverse Search with Inverted Indices – Compressed data for better memory usage [3] Feature extraction Feature aggregation Feature matching Re-ranking Preliminary results Final results [3] H. Jégou, M. Douze, C. Schmid, Product quantization for nearest neighbor search., IEEE Trans. Pattern Anal. Mach. Intell. 33 (2011) 117– 28.Data CompressionNGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 15. Verification • Geometry verification – RANSAC methods [4] – Reduce the number of good inliers Image credits: http://ai.stanford.edu/~jkrause/cars/car_dataset.html Feature extraction Feature aggregation Feature matching Re-ranking Preliminary results Final results [4] M.A. Fischler, R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM. 24 (1981) 381–395. NGUYEN ANH TUAN 東京大学・情報理 工・修士2年生
- 16. Thank you for listening