RITSU_CBVR at TRECVID-2010
نویسندگان
چکیده
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.
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