Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval

It is known that no single descriptor is powerful enough to encompass all aspects of image content, i.e. each feature extraction method has its own view of the image content. A possible approach to cope with that fact is to get a whole view of the image(object). Then using machine learning approach from user’s Relevance feedback to obtain a reduced feature. In this paper, we concentrate on some points about Biased Discriminant Analysis / Kernel Biased Discriminant Analysis (BDA/KBDA) based machine learning approach for CBIR. The

• Type: Conference
• Year: 2005
• Where: IDEAL
• Authors: Lin Mei, Gerd Brunner, Lokesh Setia, Hans Burkhardt
• Keywords: Feature Extraction Method, IDEAL 2005, Image Content, Machine Learning Approach