Joint kernel equal integration of visual features and textual terms for biomedical imaging modality classification (医用画像)

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概要

• 論文の詳細を見る

• In this paper, we describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This work is focused on the process of feature extraction from medical images and fusion the different extracted visual feature and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray or color intensity and block-based variation as global features and SIFT histogram as local feature, and the binary histogram of some predefined vocabulary words for image captions is used for textual feature. Then we combine the different features using normalized kernel functions for SVM classification. The proposed algorithm is evaluated by the provided modality dataset by ImageCLEF2010.

• 2011-05-12

著者

• Han Xian-hua

  College Of Information Science And Engineering Ritsumeikan University

• Chen Yen-wei

  College Of Information And Science Ristumeikan University

• 陳 延偉

  College of Information Science and Engineering, Ritsumeikan

• 韓 先花

  College of Information Science and Engineering, Ritsumeikan University

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