IPSJ SIG Technical Report Vol.2011-CVIM-177 No /5/ TRECVID2010 SURF Bag-of-Features 1 TRECVID SVM 700% MKL-SVM 883% TRECVID2010 MKL-SVM A

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1 1 1 TRECVID2010 SURF Bag-of-Features 1 TRECVID SVM 700% MKL-SVM 883% TRECVID2010 MKL-SVM Analysis of video data recognition using multi-frame Kazuya Hidume 1 and Keiji Yanai 1 In this study, we aim to verify the effectiveness of a multi-frame method for shot recognition proposed in recent years. In the experiments, we extract SURF, color and spatio- temporal features from the TRECVID 2010 video data, and convert them the Bag-of-Features(BoF) representation. In the multiframe method unlike the conventional method to extract features from only one keyframe, features are extracted from multiple frames which are selected from the video, and one BoF feature vector is generated by integrating these features. In the experiment, we use five kinds of concepts out of 130 TRECVID2010 target concepts and analyze recognition performance in various settings in terms of the number of frames selected from one shot. As a result, compared to the conventional method, the recognition accu- racy in classifying by SVM raised 700% at most and by MKL-SVM advance 883%. The result of MKL-SVM in all class outperformed the average of all the teams in TRECVID WEB TRECVID 1) TRECVID Web TRECVID 2. TRECVID TRECVID TREC Video Retrieval EvaluationNIST Disruptive Technology Office(DTO) TRECVID 2010 TRECVID Semantic indexing SIN SIN 1 Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications 1 c 2011 Information Processing Society of Japan

2 1 TRECVID2010 Semantic indexing TRECVID MPEG-4/H TRECVID ) 4 SIFT Multiple Kernel Learning SR-KDA(Spectral Regression combined with Kernel Discriminant Analysis) 3) MK-FDA(Multiple Kernel Fisher Discriminant Analysis) 4) TRECVID 5) 2009 SIFT (MFCC) N 1 M 2 c 2011 Information Processing Society of Japan

3 Airplane Flying Bus Hand 3 N + 1 M M N ( 1)/M + 1 N M N = 3, 5, 10, M = 30, 15, 10 M 30 / SURF Bag-of-Features SURF 2 2 Bag-of-Features SVM MKL-SVM 4 SVM RBF-χ Bag-of-Features Bag-of-Features Bag-of-Words Bag-of-Words Bag-of-Features Bag-of-Features ( 1 ) ( 2 ) k k visual words visual words codebook ( 3 ) visual words 3 c 2011 Information Processing Society of Japan

4 ( 4 ) bin ( SURF ) Bag-of-Features Bag-of-Features codebook 5.2 RGB RGB 3 Bag-of-Features visual words 5.3 SURF SURF(Speeded-Up Robust Feature) 6) SIFT 7) SIFT 128 SURF SIFT SIFT SURF 5000 TRECVID2010 Web 5.4 8) Web step1 step2 step3 step4 step5 step6 SURF SURF Delaunay Web Lucas-Kanade 9) SURF SURF Dlauney Dlauney 3 SURF 64 3 = 192 SURF N SURF N/2 N M Lucas-Kanade x,y 5 N = 5 M = = 65 SURF = Bag-of-Features Lazebnik 10) Bag-of-Features 4 c 2011 Information Processing Society of Japan

5 SURF 6. SVM(Support Vector Machine) MKL-SVM(Multiple Kernel Learning SVM) 6.1 Support Vector Machine SVM(Support Vector Machine) 2 SVM x φ(x) RBF-χ 2 K(x, y) = exp ( 1 2σ Multiple Kernel Learning i ) x i y i 2 x i + y i MKL(Multiple Kernel Learning) SVM () K K K combined (x, x ) = β jk j(x, x ) β j 0, β j = 1 (2) j=1 β j MKL MKL β j β j MKL Sonnenburg 11) SVM β j j=1 (1) Airplane Flying Boat Ship Bus Cityscape Classroom TRECVID TRECVID , TRECVID2006 (Inferred Average Precision : infap) ( )/( ) N k P recision(k) infap = 1 N N P recision(k) (3) k=1 ( )/( ) TRECVID2010 TRECVID2010 Semantic Indexing SURF 3 Bag-of-Features codebook Bag-of- Features 500 Bag-of-Features 5 c 2011 Information Processing Society of Japan

6 2 M / M M=30 M=15 M=10 Airplane Flying 4.06/ / /15.45 Boat Ship 5.40/ / /17.46 Bus 19.06/ / /14.94 Cityscape 4.80/ / /22.67 Classroom 14.11/ / / N M N=1 N=3 N=5 N=10 M=30 M=15 M=10 Airplane Flying Boat Ship Bus Cityscape Classroom = 2000 SURF = 4500 SVM MKL-SVM RBF-χ ( 1 ) ( 2 ) ( 3 ) MKL-SVM SVM M 2 MKL-SVM MKL TRECVID2010 (median) (max) 7.4 SVM N = 1 4 MKL-SVM N M TRECVID2010 N=1 N=3 N=5 N=10 M=30 M=15 M=10 median max Airplane Flying Boat Ship Bus Cityscape Classroom Airplane Flying 700% M Boat Ship M 1 M=15 MKL-SVM N MKL 7 Classroom MKL-SVM 883% Bus 4 SVM MKL-SVM Boat Ship MKL-SVM 6 c 2011 Information Processing Society of Japan

7 5 SVM 6 MKL-SVM N=1 SURF 5000 TRECVID Bag-of-Features 1% TRECVID2010 MKL-SVM SVM Classroom codebook TRECVID2010 Se- 7 N mantic indexing 2 SURF Semantic indexing N M TRECVID2010 SVM 700% MKL-SVM 883% 7 c 2011 Information Processing Society of Japan

8 情報処理学会研究報告 た また TRECVID2010 の全チームの平均値と比較した結果 MKL-SVM を使用した設 10) S.Lazebnik, C.Schmid, and J.Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. of IEEE Computer Vision and Pattern Recognition, pp , ) S.Sonnenburg, G.Ra tsch, C.Scha fer, and B.Scho lkopf. Large Scale Multiple Kernel Learning. The Journal of Machine Learning Research, Vol.7, pp , 定のいくつかで 5 クラスすべての値を上回る結果を得た 以上の点から ショット認識にお けるマルチフレーム手法の有効性を確認することができた 9. 今後の課題 実験ではより多くのフレームから特徴量を抽出した場合 逆に精度が悪くなるということ 付 が起こった 単純なショット中の位置でフレームを抽出するだけでなく より有用なフレー 録 MKL-SVM N=10 で実行した上位 15 ショットを示す 赤枠は正解のショットである ムを選択できるようにすることも重要である 例えば 時空間特徴の抽出で行ったような 選択した他のフレーム (この場合は既に抽出した前のフレーム) との差異を計算した場合に 一定以上特徴量が異なれば新たなフレームとして抽出する といったことが考えられる ま た そのフレームの差異が大きければ基本的な取得フレーム間隔を小さくし 逆に差異が小 さければ間隔を大きくする つまりフレームの変化によって取得間隔をショット毎に変更す るということもできる 参 考 文 献 1) TRECVID Home Page. 2) C.G.M. Snoek, KEA vande Sande, O.deRooij, etal. The mediamill trecvid 2008 semantic video search engine. In Proc. of TRECVID Workshop, ) D.Cai, X.He, and J.Han. Efficient kernel discriminant analysis via spectral regression. In Data Mining, ICDM Seventh IEEE International Conference on, pp IEEE, ) J.Ye, S.Ji, and J.Chen. Multi-class discriminant kernel learning via convex programming. The Journal of Machine Learning Research, Vol.9, pp , ) N.Inoue, S.Hao, T.Saito, K.Shinoda, I.Kim, and C.H. Lee. Titgt at trecvid 2009 workshop. In Proc. of TRECVID Workshop, Vol.2, ) H.Bay, T.Tuytelaars, and L.VanGool. SURF: Speeded up robust features. In Proc. of European Conference on Computer Vision, pp , ) D.G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, Vol.60, No.2, pp , ) 野口顕嗣, 柳井啓司. 動きの連続性を考慮した動画からの局所的な時空間特徴の抽出. In MIRU, ) B.D. Lucas and T.Kanade. An iterative image registration technique with an application to stereo vision. In International joint conference on artificial intelligence, Vol.3, pp Citeseer, 図8 Airplane Flying 図 9 Boat Ship 図 10 図 11 Cityscape Bus 図 12 Classroom 8 c 2011 Information Processing Society of Japan

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