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Using Bayesian classifier in relevant feedback of image retrieval
Vancouver, British Columbia, Canada November 13-November 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TAI.2000.88987912th IEEE International Conference on ...
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Zhong Su, Hongjiang Zhang, Shaoping Ma, "Using Bayesian classifier in relevant feedback of image retrieval," Tools with Artificial Intelligence, IEEE International Conference on, pp. 0258, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000.
 
 
BibTex
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@article{ 10.1109/TAI.2000.889879,
author = {Zhong Su and Hongjiang Zhang and Shaoping Ma},
title = {Using Bayesian classifier in relevant feedback of image retrieval},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2000},
isbn = {0-7695-0909-6},
pages = {0258},
doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889879},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - Using Bayesian classifier in relevant feedback of image retrieval
SN - 0-7695-0909-6
SP
EP
A1 - Zhong Su,
A1 - Hongjiang Zhang,
A1 - Shaoping Ma,
PY - 2000
KW - image retrieval; content-based retrieval; relevance feedback; Bayes methods; image classification; relevance feedback; content-based image retrieval; Bayesian classifier; positive feedback examples; negative feedback examples; query space distribution; dibbling process; image penalization; retrieval refinement process; progressive learning capability; past feedback information; query refinement
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
Zhong Su, Dept. of Comput. Sci., Tsinghua Univ., Beijing, China
Hongjiang Zhang, Dept. of Comput. Sci., Tsinghua Univ., Beijing, China
Shaoping Ma, Dept. of Comput. Sci., Tsinghua Univ., Beijing, China
Abstract: Relevance feedback is a powerful technique in content-based image retrieval (CBIR) and has been an active research area for the past few years. In this paper, we propose a new relevance feedback approach based on a Bayesian classifier, and it treats positive and negative feedback examples with different strategies. For positive examples, a Bayesian classifier is used to determine the distribution of the query space. A 'dibbling' process is applied to penalize images that are near the negative examples in the query and retrieval refinement process. The proposed algorithm also has a progressive learning capability that utilizes past feedback information to help the current query. Experimental results show that our algorithm is effective.
Index Terms:
image retrieval; content-based retrieval; relevance feedback; Bayes methods; image classification; relevance feedback; content-based image retrieval; Bayesian classifier; positive feedback examples; negative feedback examples; query space distribution; dibbling process; image penalization; retrieval refinement process; progressive learning capability; past feedback information; query refinement
Citation:
Zhong Su, Hongjiang Zhang, Shaoping Ma, "Using Bayesian classifier in relevant feedback of image retrieval," ictai, pp.0258, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000
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