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» Features for image retrieval: an experimental comparison
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WWW
2008
ACM
14 years 11 months ago
Detecting image spam using visual features and near duplicate detection
Email spam is a much studied topic, but even though current email spam detecting software has been gaining a competitive edge against text based email spam, new advances in spam g...
Bhaskar Mehta, Saurabh Nangia, Manish Gupta 0002, ...
MIR
2003
ACM
174views Multimedia» more  MIR 2003»
14 years 4 months ago
Content-based image retrieval by clustering
In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high fea...
Yixin Chen, James Ze Wang, Robert Krovetz
WIDM
2004
ACM
14 years 4 months ago
WISE-cluster: clustering e-commerce search engines automatically
In this paper, we propose a new approach to automatically clustering e-commerce search engines (ESEs) on the Web such that ESEs in the same cluster sell similar products. This all...
Qian Peng, Weiyi Meng, Hai He, Clement T. Yu
MM
2010
ACM
192views Multimedia» more  MM 2010»
13 years 11 months ago
iLike: integrating visual and textual features for vertical search
Content-based image search on the Internet is a challenging problem, mostly due to the semantic gap between low-level visual features and high-level content, as well as the excess...
Yuxin Chen, Nenghai Yu, Bo Luo, Xue-wen Chen
MM
2006
ACM
164views Multimedia» more  MM 2006»
14 years 4 months ago
Scalable relevance feedback using click-through data for web image retrieval
Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalabil...
En Cheng, Feng Jing, Lei Zhang, Hai Jin