A Survey of Content Based Image Retrieval Using Color and Texture Features

Content based image retrieval (CBIR) system that works on the basis of low level image semantics cannot be directly related to the expressive semantics that is used by humans for deciding image similarities. The low-level semantic of the image consists of color, texture, and shape of the object inside an image. Nowadays, one type of feature extraction technique cannot provide complete result, so now a combination of different feature techniques like color, texture and shape features are being used. There is a generous increase in retrieval precision when combinations of these techniques are used in an effective way. In this paper, we propose a comparison of CBIR system using different feature extraction methods; three features based on color (i.e. HSV Histogram, Color Moment) and other two features computed by applying the texture feature using Gabor Wavelet and Wavelet Transform of the image. For similarity matching between the query image and database images, Manhattan distance or City Block or L1 distance is used. The experimental results on WANG database show higher retrieval efficiency in terms of precision when compared with existing methods using color and texture features.