文献类型：会议论文

英文题名：A Novel Generalized SVM Algorithm with Application to Region-based Image Retrieval

作者：Zhang Rui-zhe[1];Yuan Jia-zheng[1];Huang Jing-hua[1];Wang Yu-jian[1];Bao Hong[1]

第一作者：张睿哲

通讯作者：Yuan, JZ[1]

机构：[1]Beijing Union Univ, Inst Informat Technol, Beijing 100101, Peoples R China

第一机构：北京联合大学智慧城市学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Informat Technol, Beijing 100101, Peoples R China.|[1141734]北京联合大学智慧城市学院;[11417]北京联合大学;

会议论文集：International Forum on Information Technology and Applications (IFITA 2009)

会议日期：MAY 15-17, 2009

会议地点：Chengdu, PEOPLES R CHINA

语种：英文

外文关键词：generalized SVM; region-based image retrieval; visual attention model; SRAG

摘要：Support vector machines (SVM) has been widely applied in the area of content-based image retrieval in order to learn high-level concepts from low-level image features. Most existing SVM based image retrieval algorithms only rely on global-based features to represent the image content, which obviously can not well reflect the image semantic content. Region-based representations are far more close to the image content. However, such representations are of variable length and the Gaussian kernel is inappropriate in this situation. In this paper, a novel generalized SVM algorithm is proposed, which takes into account both Ion-level features and structural information of the image, in order to solve the problem of region-based image retrieval via SVM framework Firstly, for a given image, salient regions are extracted and the concept of Salient Region Adjacency Graph is proposed to represent the image semantics. Secondly, based on the SRAG, a novel generalized structure kernel based SVM algorithm is constructed for content-based image retrieval. Experiments show that the proposed method shows better performance in image semantic retrieval than traditional method