Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram

Color histogram is widely used for image indexing in content-based image retrieval (CBIR). A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. Moreover, the histogram might give similar results for different kinds of images if the distributions of colors are same in the images. On the other hand, color Correlogram is efficiently used for image indexing in content-based image retrieval. Color Correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color Correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. In this paper, we propose the image bin (histogram value divisions) separation technique followed by extracting maxima of frequencies and plotting a Correlogram. At first, the histogram is first calculated for an image. After that, it is subdivided into four equal bins. Each bin is subdivided into four more bins and for every such subdivision the maxima of frequencies s calculated. This information is stored in the form of a Correlogram. The distance between Correlogram of the query image with the corresponding Correlogram of database images is calculated. The proposed algorithm is tested on a database comprising a large number of images.

1. Introduction

A huge amount of text and image data is available on the World Wide Web (WWW) for access to internet users. The amount of data especially still images and videos is increasing exponentially. The users not only want to retrieve the text data, but they would be very happy if images and videos can also be retrieved as fast as text data retrieves on the WWW. The amount of image and video data has remarkably increased and hence the demand of image retrieval systems that are able to effectively index a large amount of images and to effectively retrieve them based on their visual contents. Content Based Image Retrieval (CBIR) [1]–[4] is an interesting but difficult research topic in multimedia information technology. CBIR uses visual contents such as color, shape, texture etc to retrieve images from a huge image databases according to the user's visual queries.