IPSJ SIG Technical Report Vol.2010-CVIM-171 No /3/19 1. Web 1 1 Web Web Web Multiple Kernel Learning(MKL) Web ( ) % MKL 68.8% Extractin
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1 1. Web 1 1 Web Web Web Multiple Kernel Learning(MKL) Web ( ) % MKL 68.8% Extracting Spatio-Temporal Local Features for Classifying Web Video Shots Akitsugu Noguchi 1 and Keiji Yanai 1 Nowadays, there is a large amount of video data on the Web. Most of videos on the Web have keyword tags for text-based search. However, tags do not always reflect the contents of videos, so that content-based video search is needed. Then, in this paper, we propose a new spatio-temporal feature and a feature fusion method based on Multiple Kernel Learning (MKL) for classifying Web video shots. We made experiments on supervised shot ranking and unsupervised shot clustering. As results, we obtained 57.2% as the precision rate regarding top-ranked 200 shots using only the spatio-temporal feature. By utilizing Multiple Kernel Learning, we obtain the 68.8% precision. 1 The University of Electro-Communication Web Web Web MKL walking walking walking walking walking soccer soccer 2. Kobayashi cubic higher-order local auto-correlation(chlac) 1) Laptev 2) cuboid Dollar cuboid 3) cuboid Laptev Dollar Histgram of Orient Gradient(HoG) Histgram of Orient Flow(HoF) Kläser HoG 4) 1 c 2010 Information Processing Society of Japan
2 cuboid cuboid Web Cinbis Web Youtube 5) query word Web Liu 6) 3) SIFT 7) Adaboost Page Rank 1 Web 2 Web 3. Web 3.1 8) SURF 9) SURF SURF Lucas-Kanade 10) Delaunay 1 Dollar 3) bag of statio-temporal features(bostf) 3.2 Web 1 Web Youtube SVM MKL plsa k-means 2 running SVM Web running running MKL soccer plsa k-means 2 c 2010 Information Processing Society of Japan
3 Web Web 1 3 ( ), ( ) 1 step1 : step2 : step2-1 : SURF step2-2 : step2-3 : Delaunay step3 : step3-1 : Lucas-Kanade step3-2 : SURF diminant rotation step4 : 4 (step 1) (step 2) (step 3) (step 4) (Step 1) Web Web Liu 6) 4 3 Lucas-Kanade (Step 2) 4 (1) SURF (2) ( ) (3) Delaunay SURF SURF x SURF = 192 (Step 3) SURF 5 5( ) SURF N 3 c 2010 Information Processing Society of Japan
4 5 ( ) ( ) 6 SURF N/2 ( 1 step 2-2) 5( ) N M Lucas-Kanade i L i i 1 M N M 1 x +, x, y +, y, 5 x + x x x N=5 M=5 (M 1) = 65 dominant rotation dominant rotation 6 (x 1 y 1 ) (x 2 y 2 ) SURF dominant rotation θ (x y) 1 [ x y ] = 7 [ cosθ sinθ x2 sinθ cosθ y 2 ( ) ( ) ] x 1 x 2 y 1 y 2 1 (1) 4.2 7( ) 7( ) Multiple Kernel Learning(MKL) Multiple Kernel Learning Multiple Kernel Learning(MKL) K K combined (x, x ) = β j k j (x, x ) j=1 with β j 0, K β j = 1. (2) j=1 β j MKL 11) MKL β j β j MKL 4 c 2010 Information Processing Society of Japan
![8 9 1 257 BoSTF 5000 96 BoFr 3000 56 BoFr 3000 12) Sonnenburg SVM β j 12) 4.2.2 8 7 8 6 4 9 2 2 24 24 4 bag of frames 1 257 BoSTF 5000 2 Web [ ] [ ] positive negative batting 174 8,980 5.](/docs-images/101/151815936/images/5-0.jpg "9 14.6 31 75 running 170 7,342 6.6 13.4 28 66 walking 174 6,567 7.4 13.4 23 63 shoot 164 7,718 5.3 11.3 14 75 eating 142 3,442 7.7 7.3 22 64 jumping 160 3,130 6.6 5.")
5 BoSTF BoFr BoFr ) Sonnenburg SVM β j 12) bag of frames BoSTF Web [ ] [ ] positive negative batting 174 8, running 170 7, walking 174 6, shoot 164 7, eating 142 3, jumping 160 3, dancing 185 8, soccer , ,247 55, bag of frames(bofr) batting, running, walking, shoot, jumping, eating 6 2( ) ,179 Web Liu Wild Youtube ,179 2 Web Youtube soccer dancing 2( ) 5.2 KTH KTH 3 3 VMR Point 8) 5 c 2010 Information Processing Society of Japan
6 3 KTH Point 8) VMR MKL Liu 6) Lin 13) Gilbert 14) walking jogging running boxing waving clapping average ( ) ours CHLAC Kläser Liu 6) Lin 13), Gilbert 14) %, Liu 91.8%, Lin 93.3%, Gilbert 94.5% 15) CHLAC 1) Kläser 4) AMD Phenom II X4 3.0GHz 8G CHLAC N batting MKL eating eating eating running running 10 Web MKL walking shoot running jumping 12 plsa k-means 6 c 2010 Information Processing Society of Japan
7 : dancing soccer dancing (dancing) (sing) (acting) (others) soccer (play-far) (play-near) (talking) (others) k-means dancing 1 dancing 65.0% sing 6.0% k-means, plsa 200 plsa 4 k-means 5, 8, 10 dancing sing k-means 5, 8, 10 k-means others soccer plsa play-far play-near talking k-means plsa plsa 15 k-means dancing 7 c 2010 Information Processing Society of Japan
8 Youtube Web k-means dancing Web Web Web % % plsa k-means k-means plsa 1) T.Kobayashi and N.Otsu. A three-way auto-correlation based approach to human identification by gait. In Proc. of IEEE Workshop on Visual Surveillance, pp , ) I.Laptev and T.Lindeberg. Local descriptors for spatio-temporal recognition. In Proc.of IEEE International Conference on Computer Vision, ) P.Dollar, G.Cottrell, and S.Belongie. Behavior recognition via sparse spatio-temporal features. In Proc. of Surveillance and Performance Evaluation of Tracking and Surveillance, pp , ) A. Kläser, M. Marsza lek, and C. Schmid. A spatio-temporal descriptor based on 3dgradients. In British Machine Vision Conference, pp , sep ) R.I. Cinbins, R.Cinbins, and S.Sclaroff. Learning action from the web. In Proc.of IEEE International Conference on Computer Vision, pp , ) J.Liu, J.Luo, and M.Shah. Recognizing realistic action from videos. In Proc.of IEEE Computer Vision and Pattern Recognition, pp. 1 8, ) D.Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, pp , ) A.Noguchi and K.Yanai. Extracting spatio-temporal local features considering consecutuveness of motions. In Proc. of Asian Conference on Computer Vision(ACCV), ) B.Herbert, E.Andreas, T.Tinne, and G.Luc. Surf: Speeded up robust features. Computer Vision and Image Understanding, pp , ) B.Lucas and T.Kanade. An iterative image registration technique with an application to stereo vision. In Proc. of International Joint Conference on Artificial Intelligence, pp , ) G.R.G. Lanckriet, N.Cristianini, P.Bartlett, L.E. Ghaoui, and M.I. Jordan. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, Vol.5, pp , ) S.Sonnenburg, G.Rätsch, C.Schäfer, and B.Schölkopf. Large scale multiple kernel learning. Journal of Machine Learning Research, Vol.7, pp , ) Z.Lin, Z.Jiang, and L.S.davis. Recognizaing action by shape-motion prototype trees. In Proc.of IEEE International Conference on Computer Vision, pp , ) A. Gilbert, J. Illingworth, and R. Bowden. Fast realistic multi-action recognition using mined dense spatio-temporal features. In Proc.of IEEE International Conference on Computer Vision, pp , ),.. :, c 2010 Information Processing Society of Japan
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Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
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Automatic Collection of Web Video Shots Corresponding to Specific Actions using Web Images
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