International Journal of Engineering Sciences & Emerging Technologies, Feb. 2013. ISSN: 2231 – 6604 Volume 4, Issue 2, pp: 13-18 ©IJESET 13 HIERARCHICAL INDEXING FOR FASTER RETRIEVAL BASED ON FULL AUTOMATIC ALGORITHM Shahin Shafei, Tohid Sedghi Department of Electrical Engineering, Mahabad Branch, Islamic Azad University, Mahabad, Iran Shahin_shafei1987@yahoo.com ABSTRACT In this paper, we aim to take advantage of texture properties of images to improve the performance of the image indexing and retrieval algorithm. Beside that a framework for segmentation of the image which results in categorizing and identifying the object in image is presented. Image retrieval based on region is one of the most promising and active research directions in recent years. In this study, the combination of the texture features and regioing method provide an efficient feature set for image retrieval. Experimental results exhibit that the proposed method yield higher retrieval accuracy than some conventional methods even though its feature vector length and feature generating time of query image is less than those of other approaches. I. INTRODUCTION The problem of finding images from data base according to their content has been the subject of a significant amount of research in the last decade. Previous studies [1-5] prove that region segmentation will produce better results. Human visual perception is more effective than any machine vision systems for extracting semantic information from image; hitherto no specific system has been suggested with the ability of extracting object individually. We introduce a new idea, hence object detection has been obtained as main contribution of this paper and a new feature extraction based on wavelet analysis is presented. In this paper Expectation Maximization (EM) algorithm is utilized to segment image into different regions. A new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space is presented .In addition the EM algorithm performs automatic segmentation based on image features [6]. EM iteratively models the joint distribution of color and texture with a mixture of Gaussians. The resulting pixel cluster memberships provide a segmentation of the image. After the image is segmented into regions, system select the region where contain main object. More over a description of chosen region, based on novel feature extraction is produced [7]. Object-based image retrieval is not limited by the averaging properties associated with analyzing the entire image and can use local properties. Further, the computational time of the presented method is less than the previously presented methods which is an advantage in CBIR systems. The other option of the proposed system is that the user can access the regions directly in order to see the segmentation of the query image and specify which aspects of the image are important to the query. The deficiency of traditional retrieval systems is due to either both image representation and method of accessing those representations to find images, while users generally want to find images containing particular objects [8-12].Most existing image retrieval systems represent images based only on their low-level features, with little regard for the spatial organization of those features. Systems based on user querying are often unintuitive and offer little help in understanding why certain images were returned and how to refine the query. Often the user knows only that he has submitted a query for, say, a horse and retrieved very few pictures of horses in return [13-15].For general image collections, there are currently no systems that can automatically classify images or recognize the objects they contain. In particular, this letter demonstrates how the segmentation and new feature extraction can considerably enhance object based