Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning

Szu-Hao Huang*, Qi Jiunn Wu, Shang Hong Lai

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In this paper, we propose a novel paired feature learning system for relevance feedback based image retrieval. To facilitate density estimation in our feature learning system, we employ an ID3-like balance tree quantization method to preserve most discriminative information. In addition, we map all training samples in the relevance feedback onto paired feature spaces to enhance the discrimination power of feature representation. Furthermore, we replace the traditional binary classifiers in the AdaBoost learning algorithm by Bayesian weak classifiers to improve its accuracy, thus producing stronger classifiers. Experimental results on content-based image retrieval show improvement of each step in the proposed learning system.

Original languageEnglish
Pages (from-to)660-670
Number of pages11
JournalLecture Notes in Computer Science
StatePublished - 17 Oct 2005
Event4th International Conference on Image and Video Retrieval, CIVR 2005 - , Singapore
Duration: 20 Jul 200522 Jul 2005


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