CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Web Image Mining Based on Modeling Concept-Sensitive Salient Regions
Jing Liu; Qingshan Liu; Jinqiao Wang; Hanqing Lu; Songde Ma
2006
Conference NameIEEE International Conference on Multimedia and Expo
Source PublicationProceedings of the 2006 IEEE International Conference on Multimedia and Expo
Conference DateJuly 9-12, 2006
Conference PlaceToronto, Ontario, Canada
AbstractIn this paper, we propose a probabilistic model for Web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-level understanding of image semantics to bridge the semantic gap existing in the field of image mining and retrieval. With the help of a popular search engine, semantically relevant images are collected, and concept-sensitive salient regions are extracted automatically based on an attention model. Then the semantic concept model is learned from the joint distribution of all salient regions with Gaussian mixture model and expectation-maximization algorithm. In addition, by incorporating semantically irrelevant un-salient regions as negative samples, the discriminative power of the solution is further enhanced. Experiments demonstrate the encouraging performance of the proposed method
KeywordGaussian Processes Data Mining Expectation-maximisation Algorithm Image Retrieval Search Engines Semantic Web Gaussian Mixture Model Web Image Mining Concept-sensitive Salient Region Model Exoexpectation-maximization Algorithm Image Retrieval Image Semantics Probabilistic Model Search Engine Bridges Detectors Html Humans Image Analysis Image Retrieval Image Segmentation Pixel Search Engines Skin
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13455
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorJing Liu
Recommended Citation
GB/T 7714
Jing Liu,Qingshan Liu,Jinqiao Wang,et al. Web Image Mining Based on Modeling Concept-Sensitive Salient Regions[C],2006.
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