A fused features approach on Content-Based Image Retrieval based on Fuzzy rule-set

Research in content-based image retrieval today is a lively discipline, expanding in breadth. Discovering new and innovative processes of locating a desired image from an expanding collection of images has been a major area of interest for many professional fields. This has led to the increasing use of a technique called Content Based Image Retrieval which is used to retrieve images based on low-level features which are automatically driven such as color, shape and texture. CBIR draws many of its methods from the field of image processing and computer vision and can be regarded as subset of that field. Using this technique, the entire database may be searched to find the most closely matching image. In this paper, color and texture features of an image have been used to retrieve all the similar images from the image database. For this purpose, Block Color Histogram as color feature and Gray-level Co-occurrence Matrix (GLCM) and Gabor wavelets as texture features have been used. The proposed approach is very efficient since it uses the prominent and distinct features of an image useful for effective image retrieval using fuzzy heuristics according to human visual system.