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» Web image retrieval reranking with multi-view clustering
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WWW
2009
ACM
14 years 5 months ago
Web image retrieval reranking with multi-view clustering
General image retrieval is often carried out by a text-based search engine, such as Google Image Search. In this case, natural language queries are used as input to the search eng...
Mingmin Chi, Peiwu Zhang, Yingbin Zhao, Rui Feng, ...
MM
2009
ACM
137views Multimedia» more  MM 2009»
13 years 11 months ago
Lightweight web image reranking
Web image search is inspired by text search techniques; it mainly relies on indexing textual data that surround the image file. But retrieval results are often noisy and image pro...
Adrian Popescu, Pierre-Alain Moëllic, Ioannis...
CIKM
2010
Springer
13 years 3 months ago
Visual-semantic graphs: using queries to reduce the semantic gap in web image retrieval
We explore the application of a graph representation to model similarity relationships that exist among images found on the Web. The resulting similarity-induced graph allows us t...
Barbara Poblete, Benjamin Bustos, Marcelo Mendoza,...

Publication
1763views
14 years 29 days ago
Reranking with Contextual dissimilarity measures from representational Bregman k-means
We present a novel reranking framework for Content Based Image Retrieval (CBIR) systems based on con-textual dissimilarity measures. Our work revisit and extend the method of Perro...
Olivier Schwander, Frank Nielsen
PAMI
2012
11 years 7 months ago
IntentSearch: Capturing User Intention for One-Click Internet Image Search
—Web-scale image search engines (e.g. Google Image Search, Bing Image Search) mostly rely on surrounding text features. It is difficult for them to interpret users’ search int...
Xiaoou Tang, Ke Liu, Jingyu Cui, Fang Wen, Xiaogan...