A hierarchical discriminant analysis framework for content-based image retrieval system for industrial applications

Content-based image retrieval (CBIR) systems have drawn interest from many researchers in recent years. One of the potential applications of CBIR is in industrial areas where the most relevant drawings or images can be retrieved speedily without the need to memorize any file name or specific key-words. To increase the retrieval speed, most of the systems pre-process the stored images by extracting a set of predefined features. Such scheme only works well for the server type database systems where the images have been stored previously. It is not feasible for systems that analyze images in real-time where the images are stored or added on an ongoing basis. For instance, personal image search engine for the World-Wide-Web is such an example. In this paper, the authors propose a multi-layer statistical discriminant framework which is able to select the most appropriate features to analyze newly received images thereby improving the retrieval accuracy and efficiency.