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1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TRECVID2012 Instance Search {sakata,matozaki}@m.cs.osakafu-u.ac.jp, {kise,masa}@cs.osakafu-u.ac.jp TRECVID 2012 Instance Search BoF 18.2% Mean Average Precision 24 4 TRECVIDInstance Search 1. TRECVID(TREC Video Retrieval Evaluation) NIST(National Institute of Standards and Technology) TRECVID Instance Search(INS) INS INS INS [1] Bag-of-Features(BoF) BoF [2] BoF Visual Word [3] Visual Word Visual Word BoF [4] INS 18.2% Mean Average Precision(MAP) 24 4 INS 1

2 (a)object() shot Run Run 1 4 Run Avarage Precision (AP) AP Mean AP(MAP) 3. (b)location() (c)person(stephen Colbert) 1 (::) 2. Instance Search(INS) TRECVID2012 INS 2. 1 Flickr video available under Creative Commons licenses for research 10 shot shot shot 74,958 shot ID 1232 shot PERSON()OBJECT()LOCATION( ) PERSON 1 OBJECT 15 LOCATION TRECVID2012 INS TRECVID [5] HSV SIFT BoFGabor WaveletHOG 9 10 CMG(Color Moment Grid) LBP(Local Binary Pattern) SIFTColor SIFTOpponent SIFT BoF multi-bag SVM SIFT SIFT BoF Hamming Embedding Multiple Assignments BoF BoF Delaunay Triangulation NTT-NII 192 Color SIFT BM 25 SIFT Color SIFT 50 Amazon Elastic Compute Cloud 2

3 2 Shot Key frames Extract features Shot ID 0 1 Time[sec] Shot ID DB 3 4. INS 2 [4] 4. 1 DB INS 2 3 Lanczos 4. 3 [4] shot d x = (x 1, x 2,, x d ) x 1 d (d < = d ) { 1 if xj θ j > u j = = 0, (0 < = j < = d) (1) 0 otherwise, 2 u = (u 1, u 2,, u d ) θ j x j ( d 1 ) H index = u i2 i mod H size (2) i=0 shot ID H size 2 d ID ID q X q X k 3

4 X X shot ID INS shot C s shot k shot k (0.95) k 1 / C s n multiprobing e q = (q 1,..., q d ) q j µ j < = e j u j u j = 1 u j b 2 b 4. 4 shot r 1 1 ID m(< 1) 4. 5 OpponentSIFT [6] OpponentSIFT Harris Laplace detector [7] Harris Laplace detector OpponentSIFT RGB Opponent [8] O 1 O 2 O 3 1 *IMP.h f e *IMP.h f e2 IMP.h e1 *IMP.h e2 *IMP.h e3 = *TRECVID2012 R G 2 R+G 2B 6 R+G+B 3 2 MAP[%] *IMP.h f e *IMP.h f e IMP.h e *IMP.h e *IMP.h e (3) O 3 HSV O 1 O 2 O 1 O 3 SIFT OpponentSIFT ColorDescriptor software v3.0 [9] 5. 1 d = 32 k = 20 n = 5 b = 10 r = 10shot m = run 2 IMP.h e % MAP 79Run INS MAP 2 3 MAP 4

5 Average Precision[%] IMP.h_e2 IMP.h_f_e1 Median Top Object Background Feedback query number 4 Accuracy[%] query number 7 (a) ( ) (b) ( ) 5 Run MAP AP 4 AP AP 9052() 9063() Run AP 5 5(a) 9056()9060( )9068(PUMA ) MAP 2 5(b) 9048()9054()9067( ) % MAP 9052( ) (a) 5

6 50 19 Rank Number of local features 8 shot Mean Average Precision[%] k 9 k MAP IMP.h f e2 50 shot shot shot 50 shot [10] 6. 3 k MAP INS 1 shot 1 k k k 7. TRECVID 2012 INS MAP 18.23% 79Run (B)( ) [1] Z. Zhao, Y. Zhao, Y. Hua, W. Wang, D. Wan, G. Jia, Z. Li, F. Su and A. Cai: Bupt-mcprl at trecvid 2012, TRECVID 2012 Workshop Notebook (2012). [2] J. M. Barrios and B. Bustos: Instance search based on parallel approximate searches, TRECVID 2012 Workshop Notebook (2012). [3] D. Nistér and H. Stewénius: Scalable Recognition with a Vocabulary Tree (2006). [4] K. Kise, K. Noguchi and M. Iwamura: Robust and efficient recognition of low-quality images by cascaded recognizers with massive local features, Proceedings of the 1st International Workshop on Emergent Issues in Large Amount of Visual Data (WS-LAVD2009), pp (2009). [5] TREC video retrieval evaluation. [6] K. E. A. van de Sande, T. Gevers and C. G. M. Snoek: Color Descriptors for Object Category Recognition, European Conference on Color in Graphics, Imaging and Vision, pp (2008). [7] K. Mikolajczyk and C. Schmid: Scale & Affine Invariant Interest Point Detectors, Int. J. Comput. Vision, 60, pp (2004). [8] J. van de Weijer and T. Gevers: Boosting saliency in color image features, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 05) - Volume 1 - Volume 01, CVPR 05, Washington, DC, USA, IEEE Computer Society, pp [9] K. E. A. van de Sande, T. Gevers and C. G. M. Snoek: Evaluating color descriptors for object and scene recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 9, pp (2010). [10] A. Singhal, C. Buckley, M. Mitra and A. Mitra: Pivoted document length normalization, Proc. SIGIR, ACM Press, pp (1996). 6

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