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» Image Ranking and Retrieval Based on Multi-Attribute Queries
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SIGIR
2008
ACM
15 years 6 months ago
Learning to reduce the semantic gap in web image retrieval and annotation
We study in this paper the problem of bridging the semantic gap between low-level image features and high-level semantic concepts, which is the key hindrance in content-based imag...
Changhu Wang, Lei Zhang 0001, Hong-Jiang Zhang
SIGIR
2011
ACM
14 years 9 months ago
A boosting approach to improving pseudo-relevance feedback
Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback...
Yuanhua Lv, ChengXiang Zhai, Wan Chen
WWW
2009
ACM
16 years 7 months ago
Learning to tag
Social tagging provides valuable and crucial information for large-scale web image retrieval. It is ontology-free and easy to obtain; however, irrelevant tags frequently appear, a...
Lei Wu, Linjun Yang, Nenghai Yu, Xian-Sheng Hua

Publication
1763views
16 years 3 months 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
ICSNW
2004
Springer
238views Database» more  ICSNW 2004»
16 years 2 days ago
Knowledge Sifter: Agent-Based Ontology-Driven Search over Heterogeneous Databases Using Semantic Web Services
Knowledge Sifter is a scaleable agent-based system that supports access to heterogeneous information sources such as the Web, open-source repositories, XML-databases and the emergi...
Larry Kerschberg, Mizan Chowdhury, Alberto Damiano...