Content Based Image Retrieval Using Dominant Color, Texture and Shape

In these days people are interested in using digital images. So the size of the image database is increasing enormously. Lot of interest is paid to find images in the database. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color, texture and shape are three important visual features of an image. In this paper we propose an efficient image retrieval technique which uses dynamic dominant color, texture and shape features of an image. An image is uniformly divided into 8 coarse partitions as a first step. After the above coarse partition, the centroid of each partition (“color Bin” in MPEG-7) is selected as its dominant color. Texture of an image is obtained by using Gray Level Co-occurrence Matrix (GLCM). Color and texture features are normalized. Shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the color and texture features of an image in conjunction with the shape features provide a robust feature set for image retrieval.Weighted Euclidean distance of color, texture and shape features is used in retrieving the similar images. The efficiency of the method is demonstrated with the results.