Enhanced visual categorization performances by incorporation of simple features into bim features
Recent studies have demonstrated that Biologically Inspired Model features (BIM) are effective for object or scene categorization. However, according to BIM's forming mechanism, it may not hit the typical pattern due to its blind feature selection. In order to provide more informative pattern information for different visual object classes, a large number of prototypes have to be used in describing images. This leads to huge redundancy which may decrease categorization accuracy. Thus, improving BIM feature's performance by just increasing the number of prototypes is not adequate. In this paper, we propose an integrated approach to address this problem. In our approach, some simple non-biological features such as color histogram and Edge Orientation Histogram (EOH) are incorporated into BIM for discriminative image representation. Experimental results have shown that combination of BIM and simple features can improve visual categorization performances significantly.