Abstract: 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 image retrieval techniques used are color dominant, texture and histogram features. Using that technique, as a first step an image can be uniformly divided into coarse partitions. GLCM (Gray Level Co-occurrence Matrix) is used here for texture representation for image retrieval based. 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. Color histogram is the most important color representation factor used in image processing. Color histogram yields better retrieval accuracy. Histogram finds out the number of pixels in gray level. After that we are applying Euclidean distance, Neural Network, Target search methods algorithm and K-means clustering algorithm for retrieval of images from the database and making a comparison based approach between them to see which method helps in fast retrieval of images in terms of distance and time.

Keywords: Color feature extraction, Texture feature extraction, Histogram based extraction, image database, Euclidean distance, neural network, Neighbouring Divide-and-Conquer Method and Global Divide-and-Conquer Method, K-means clustering, Threshold=15000.