TokyoTechCanon at TRECVID 2013

We aim at developing a high-performance system using Gaussian-mixture-model (GMM) supervectors and tree-structured GMMs [6, 7, 8] for the semantic indexing task [1, 2, 3, 4]. GMM supervectors corresponding to six types of audio and visual features are extracted from video shots. Tree-structured GMMs reduce the computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parameters while keeping accuracy at high levels. This year, we improve our re-scoring method using video-clip scores by introducing a scaling parameter. Here, the video-clip score is defined as the maximum value of shot scores among all the shots in a video clip. Our best result was 28.4% in terms of Mean InfAP, which was ranked third among participating teams in the semantic indexing task.

[1]  Georges Quénot,et al.  TRECVID 2015 - An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics , 2011, TRECVID.

[2]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[3]  Paul Over,et al.  Creating HAVIC: Heterogeneous Audio Visual Internet Collection , 2012, LREC.

[4]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[5]  Ivan Laptev,et al.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

[6]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[7]  Paul Over,et al.  High-level feature detection from video in TRECVid: a 5-year retrospective of achievements , 2009 .

[8]  Steve Young,et al.  The HTK book , 1995 .

[9]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[10]  Cordelia Schmid,et al.  Coloring Local Feature Extraction , 2006, ECCV.

[11]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[12]  Koichi Shinoda,et al.  Event detection in consumer videos using GMM supervectors and SVMs , 2013, EURASIP J. Image Video Process..

[13]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Koichi Shinoda,et al.  A fast MAP adaptation technique for gmm-supervector-based video semantic indexing systems , 2011, ACM Multimedia.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[17]  Pavel Zemcík,et al.  Annotating Images with Suggestions - User Study of a Tagging System , 2012, ACIVS.

[18]  Georges Quénot,et al.  Re-ranking by local re-scoring for video indexing and retrieval , 2011, CIKM '11.

[19]  Steve Young,et al.  The HTK book version 3.4 , 2006 .

[20]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[21]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[23]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[24]  Stéphane Ayache,et al.  Video Corpus Annotation Using Active Learning , 2008, ECIR.

[25]  O. Chum,et al.  ENHANCING RANSAC BY GENERALIZED MODEL OPTIMIZATION Onďrej Chum, Jǐ , 2003 .

[26]  Koichi Shinoda,et al.  TokyoTech+Canon at TRECVID 2011 , 2011, TRECVID.

[27]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[28]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[29]  A. Smeaton,et al.  TRECVID 2013 -- An Overview of the Goals, Tasks, Data, Evaluation Mechanisms, and Metrics | NIST , 2011 .

[30]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[32]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[33]  Koichi Shinoda,et al.  High-Level Feature Extraction Using SIFT GMMs and Audio Models , 2010, 2010 20th International Conference on Pattern Recognition.

[34]  Koichi Shinoda,et al.  A Fast and Accurate Video Semantic-Indexing System Using Fast MAP Adaptation and GMM Supervectors , 2012, IEEE Transactions on Multimedia.