SVM-based relevance feedback using random subspace method

Relevance feedback (RF) schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM based RF is often poor when the number of labeled feedback samples is small. In order to solve this problem, we propose an RF algorithm using the random subspace method. The algorithm can overcome the classifier unstable and over-fitting problems that are common to the SVM based RF. Through extensive experiments on 17, 800 images, the proposed algorithm is shown to outperform existing algorithms significantly.