Integrating Relevance Feedback in Boosting for Content-Based Image Retrieval
Jie Yu, Yijuan Lu, Yuning Xu, Nicu Sebe, Qi Tian · 2007
Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In this paper, we propose a novel interactive boosting framework to integrate user feedback into boosting scheme and bridge the gap between high-level semantic concept and low-level image features. Our method achieves more performance improvement from the relevance feedback than AdaBoost does because human judgment is accumulated iteratively to facilitate learning process. It also has obvious advantage over the classic relevance feedback method in that the classifiers are trained to pay more attention to wrongfully predicted samples in user feedback through a reinforcement training process. An interactive boosting scheme called i.Boost is implemented and tested using adaptive discriminant projection (ADP) as base classifiers, which not only combines but also enhances a set of ADP classifiers into a more powerful one. To evaluate its performance, several applications are designed on UCI benchmark data sets, Harvard, UMIST, ATT facial image data sets and COREL color image data sets. The proposed method is compared to normal AdaBoost, classic relevance feedback and the state-of-the-art projection-based classifiers. The experiment results show the superior performance of i.Boost and the interactive boosting framework.