Improved AdaBoost-Based Image Retrieval with Relevance Feedback via Paired Feature Learning

摘要

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.