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» Image Ranking and Retrieval Based on Multi-Attribute Queries
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SIGIR
2008
ACM
15 years 17 days 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
146
Voted
SIGIR
2011
ACM
14 years 3 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
87
Voted
WWW
2009
ACM
16 years 1 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
142
Voted

Publication
1763views
15 years 9 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
158
Voted
ICSNW
2004
Springer
238views Database» more  ICSNW 2004»
15 years 6 months 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...