Web Image Mining Based on Modeling Concept-Sensitive Salient Regions
Jing Liu; Qingshan Liu; Jinqiao Wang; Hanqing Lu; Songde Ma
2006
会议名称IEEE International Conference on Multimedia and Expo
会议录名称Proceedings of the 2006 IEEE International Conference on Multimedia and Expo
会议日期July 9-12, 2006
会议地点Toronto, Ontario, Canada
摘要In 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
关键词Gaussian 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
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13455
专题模式识别国家重点实验室_图像与视频分析
通讯作者Jing Liu
推荐引用方式
GB/T 7714
Jing Liu,Qingshan Liu,Jinqiao Wang,et al. Web Image Mining Based on Modeling Concept-Sensitive Salient Regions[C],2006.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Jing Liu]的文章
[Qingshan Liu]的文章
[Jinqiao Wang]的文章
百度学术
百度学术中相似的文章
[Jing Liu]的文章
[Qingshan Liu]的文章
[Jinqiao Wang]的文章
必应学术
必应学术中相似的文章
[Jing Liu]的文章
[Qingshan Liu]的文章
[Jinqiao Wang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。