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» Learning an image manifold for retrieval
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MIR
2006
ACM
145views Multimedia» more  MIR 2006»
14 years 1 months ago
Similarity learning via dissimilarity space in CBIR
In this paper, we introduce a new approach to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the fe...
Giang P. Nguyen, Marcel Worring, Arnold W. M. Smeu...
CIVR
2009
Springer
212views Image Analysis» more  CIVR 2009»
14 years 2 months ago
NUS-WIDE: a real-world web image database from National University of Singapore
This paper introduces a web image dataset created by NUS’s Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,...
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie L...
AAAI
2010
13 years 9 months ago
G-Optimal Design with Laplacian Regularization
In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attem...
Chun Chen, Zhengguang Chen, Jiajun Bu, Can Wang, L...
WWW
2011
ACM
13 years 2 months ago
Learning to re-rank: query-dependent image re-ranking using click data
Our objective is to improve the performance of keyword based image search engines by re-ranking their baseline results. To this end, we address three limitations of existing searc...
Vidit Jain, Manik Varma
PRIS
2001
13 years 9 months ago
Relevance Feedback in Content-based Image Search
: Content-based image retrieval (CBIR) is a research area dedicated to address the retrieve and search multimedia documents for digital libraries. Relevance feedback is a powerful ...
HongJiang Zhang