RITSU_CBVR at TRECVID-2010

ion In this paper, we describe our first participation for the semantic indexing task at TRECVID 2010 [1]. We focus on extraction multiple low-level feature sets and a fusion method. In our system, six features are extracted for all the predefined concepts from the keyframes, including global features (RGB color histogram, HSV color histogram, edge histogram, Grey Level Co-occurrence Matrix, GIST) and a local feature (gray-scale SIFT). SVM-based classifiers are trained by utilizing these features and multiple feature weighted fusion of the classification results are used as a baseline. In this year, only one run was submitted to “full” submission: F_A_IIPLA_Ritsu_CBVR_1: Multiple feature weighted fusion of classification results based on global features and local features are utilized. SVM classifiers are trained on the images provided by the collaborative annotation in TRECVID 2010.

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

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Han Xian-Hua,et al.  Hierarchical Classifier with Multiple Feature Weighted Fusion for Scene Recognition , 2010 .

[4]  Yen-Wei Chen,et al.  Hierarchical classifier with multiple feature weighted fusion for scene recognition , 2010, The 2nd International Conference on Software Engineering and Data Mining.

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

[6]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[9]  Sarod Yatawatta,et al.  2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) , 2011, ICIP 2011.

[10]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[11]  Shamik Sural,et al.  Segmentation and histogram generation using the HSV color space for image retrieval , 2002, Proceedings. International Conference on Image Processing.

[12]  Chee Sun Won,et al.  Efficient use of local edge histogram descriptor , 2000, MULTIMEDIA '00.

[13]  Yen-Wei Chen,et al.  Image Categorization by Learned PCA Subspace of Combined Visual-words and Low-level Features , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[14]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Sadaoki Furui,et al.  A stream-weight optimization method for multi-stream HMMs based on likelihood value normalization , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[16]  Xiaojun Qi,et al.  Incorporating multiple SVMs for automatic image annotation , 2007, Pattern Recognit..

[17]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.