Youtube. Mining Specific Actions from Youtube Video with Spatio-Temporal Features
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1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. Youtube DOHANG NGA {dohang,yanai}@mm.cs.uec.ac.jp Web Web Youtube Web2.0 bag-of-features VisualRank VisualRank Web %, % Web VisualRank Web2.0 Mining Specific Actions from Youtube Video with Spatio-Temporal Features DO HANG NGA and Keiji YANAI The University of Electro-Communication, Choufugaoka 1 5 1, Choufu, Tokyo, Japan {dohang,yanai}@mm.cs.uec.ac.jp Abstract In this paper, we present a new method of automatically extracting from tagged Web videos the video-shots correspond to specific actions with just inputing the action keywords such as walking, eating etc. Key words Spatio-Temporal Feature, Web video, unsupervised learning Web Web YouTube Web Web Web eating eating eating eating eating Web Web 15 [1] Web 1
2 1.2 6 Web2.0 VisualRank 1 Web running marathon running marathon running marathon MIL Satkin [8] multi-class SVM Cinbis [9] Web YouTube Niebles [2] Dollar [3] plsa KTH [4] Web Web [5] 91% Liu [6] YouTube 11 Wild YouTube [3] SIFT Adaboost PageRank Liu PageRank Liu Cinbis [7] Multiple Instance Learning (MIL) 2 Web Web2.0 [10] Web2.0 VisualRank Web Web2.0 Web2.0 VisualRank 2
3 Web2.0 Web2.0 [10] 0() 1( ) Web API 1000 ID Web2.0 Web2.0 (eating running ) 4.2 Youtube API 1000 ID Web RGB Noguchi [5] ( [11] ) step1 : step2 : step2-1 : SURF step2-2 : step2-3 : Delaunay step3 : step3-1 : Lucas-Kanade step3-2 : SURF diminant rotation step4 : Noguchi SURF SURF Lucas-Kanade Delaunay Lucas-Kanade Lucas-Kanade bag-of-features(bof) BoF 1 BoF 4.1 BoF step1 : step2 : codebook step3 : codebook a ) (step 1) = b ) codebook (step 2) codebook k-means visual words codebook c ) (step 3) codebook visual words visual words codebook 4.6 MKL cross-validation 3
4 [5] 2:1:1 4.7 VisualRank Visual- Rank [12] VisualRank 2 (1) 3 Youtube s(h 1, H 2 ) = H i=1 min(h 1i, H 2i ) (1) H H (2) S combined = w st S st + w m S m + w v S v (2) where w st = 1 2, wm = 1 4, wv = 1 4 st m v S w VisualRank Web2.0 VisualRank (3) V R = V R ds + (1 d)q (3) { 1 where q j =, j < m m 0, j > = m m m = 1000 VisualRank Web Web Web2.0 VisualRank d d > = 0.8 d = Youtube batting, running marathon, walking street, shoot football, jumping trampoline, eating ramen 6 ( 3) 1 VisualRank VisualRank 2000 (4) Web2.0 MS(V i ) = S(V i AS 3 + P (4) NS/2, 20 < NS < 50 where P = NS/3, 50 < = NS < 90 40, NS > = 90 MS S Web2.0 AS NS 20 P Web2.0 1 [5] 100 [5] 1 [5] batting % eating % jumping % running % shoot % walking % 1, : 80% YouTube Web2.0 Web VisualRank 2 3 Web2.0 4
5 2 3 Web batting 68% 8% eating 42% 19% jumping 76% 19% running 18% 17% shoot 6% 5% walking 7% 8% 36.17% 12.67% Web batting 100% 86.7% 72% 68% 2% eating 50% 33.3% 40% 47% 0% jumping 80% 83.3% 82% 82% 5% running 30% 33.3% 30% 34% 5% shoot 20% 26.7% 32% 33% 1% walking 20% 23.3% 20% 21% 1% 50% 47.8% 46% 47.5% 2.2% 4 Web batting 70% 83.3% 76% 66% 3% eating 50% 60% 48% 41% 1% jumping 100% 90% 86% 85% 9% running 30% 40% 42% 38% 8% shoot 50% 53.3% 38% 29% 1% walking 20% 50% 40% 38% 2% 53.3% 62.8% 55% 48.7% 4% Web2.0 running, walking, shoot 3 Web2.0 batting jumping ( 68% 79%) shoot walking ( 6% 7%) VisualRank Web2.0 Web2.0 Web % % 62.8% 5. Web % % % 100 4% VisualRank Web2.0 Web % VisualRank Web Web [1] J. Sun, X. Wu, S. Yan, L.F. Cheong, T.S. Chua, and J. Li. Hierarchical spatio-temporal context modeling for action recognition. CVPR 2009, pp , [2] J. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision, Vol. 79, pp , [3] P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In Visual Surveillance and Performance Evaluation of Tracking and Surveillance, nd Joint IEEE International Workshop on, pp , [4] I. Laptev. On space-time interest points. International Journal of Computer Vision, Vol. 64, pp , [5] A. Noguchi and K. Yanai. A surf-based spatio-temporal feature for feature-fusion-based action recognition. In Proc. of ECCV WS on Human Motion: Understanding, Modeling, Capture and Animation, [6] J. Liu, L. Jiebo, and M. Shah. Recognizing realistic actions from videos in the wild. In CVPR 2009, pp , [7] N. Ikizler-Cinbis and S. Sclaroff. Object, scene and actions: Combining multiple features for human action recognition. In ECCV 2010, Vol. 6311, pp [8] S. Satkin and M. Hebert. Modeling the temporal extent of actions. In ECCV 2010, Vol. 6311, pp [9] N. Ikizler-Cinbis, R.G. Cinbis, and S. Sclaroff. Learning actions from the web. In ICCV 2009, pp , [10] Q. Yang, X. Chen, and G. Wang. Web 2.0 dictionary. In Proc. of ACM International Conference on Image and Video Retrieval, pp , [11],,.. (MIRU2010), [12] Y. Jing and S. Baluja. Visualrank: Applying pagerank to large-scale image search. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, pp ,
6 4 batting 10 7 batting 10 5 jumping 10 8 jumping 10 6 eating 10 9 eating 10 6
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