Image retrieval using contribution-based clustering algorithm with different feature extraction techniques

CBIR is the process where searching of image is done by using content of image. That content is color, texture, shape and layout of an image. In this paper we use different feature extraction techniques with contribution based clustering algorithm to retrieve the similar images from database. The techniques are RGB color histogram, RGB color histogram with Canny edge detection and local binary pattern (LBP). LBP is a new rotation invariant texture measure which is theoretically simply but powerful. In this paper, the LBP operator was used to calculate texture features. Experimental results have been tested on the test dataset of about 771 images from the Washington University database. CLBP improves the Precision, recall and f-measure value of image retrieval.