Comparative Based Analysis of Feature Extraction Approach for Content Based Image Retrieval

Abstract: People recently are interested in using digital images. Hence developing an efficient technique for finding the images has become a great need. Content Based Image Retrieval (CBIR) is a significant and increasingly popular approach that helps in the retrieval of image data from a huge collection. Image representation based on certain features helps in retrieval process. Three important visual features of an image include Color, Texture and Histogram. Here we are using an efficient image retrieval technique which uses dynamic dominant color, texture and histogram features of an image. Using that technique, as a first step an image can be uniformly divided into coarse partitions. The centroid of each partition will be selected as its dominant color after the above coarse partition. A texture representation for image retrieval based on GLCM (Gray Level Co-occurrence Matrix) can be used. Although a precise definition of texture is untraceable, the notion of texture generally refers to the presence of a spatial pattern that has some properties of homogeneity. In particular, the homogeneity cannot result from the presence of only a single color in the regions, but requires interaction of various colors. Color histogram is the most commonly used color presentation. Color histogram yields better retrieval accuracy. Histogram of an image represents relative frequency of occurrence of various gray levels. It is a spatial domain technique. Impressions of histogram can be conveyed by color or intensity patterns, or texture, from which a geometrical representation can be derived. After that we are applying target search methods algorithm and making a comparison based approach between two algorithms Neighboring Divide-and-Conquer Method and Global Divide-and-Conquer Method to see which method helps in fast retrieval of images.

Keywords: Color feature extraction, Texture feature extraction, Histogram based extraction, image database, Euclidean distance, neural network, Neighboring Divide-and-Conquer Method and Global Divide-and-Conquer Method.