A hierarchical nonparametric discriminant analysis approach for a content-based image retrieval system

This paper proposes a hierarchical nonparametric discriminant analysis (HNDA) content-based image retrieval (CBIR) system for e-business applications. It has the potential to become an important and integral component for future e-business applications. Developments in CBIR have drawn interest from many researchers and practitioners in recent years. The challenge is how to retrieve the most appropriate or relevant images at the fastest speed. To increase the retrieval speed, most of the systems pre-process the stored images and extract out the essential features. Such scheme only works well for the server type database system. Such approach is not feasible for systems that analyze images in real-time. In this paper, a hierarchical multi-layer statistical discriminant framework is proposed. The system is able to select the most appropriate features by analyzing the newly received images, and then apply a relevance feedback (RF) approach to improve the retrieval accuracy. As the number of features being analyzed is less, an improvement in performance is achieved