Amruta Dubewar et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.935-938 www.ijera.com 935 | Page Query Adaptive Image Retrieval System Amruta Dubewar*, Prof. Snehal Bhosale** *(Department of Electronics and Telecommunication RMD Sinhgad School of Engineering, Warje, Pune) ** (Department of Electronics and Telecommunication, RMD Sinhgad School of Engineering, Warje, Pune) ABSTRACT Images play a crucial role in various fields such as art gallery, medical, journalism and entertainment. Increasing use of image acquisition and data storage technologies have enabled the creation of large database. So, it is necessary to develop appropriate information management system to efficiently manage these collections and needed a system to retrieve required images from these collections. This paper proposed query adaptive image retrieval system (QAIRS) to retrieve images similar to the query image specified by user from database. The goal of this system is to support image retrieval based on content properties such as colour and texture, usually encoded into feature vectors. In this system, colour feature extracted by various techniques such as colour moment, colour histogram and autocorrelogram and texture feature extracted by using gabor wavelet. Hashing technique is used to embed high dimensional image features into hamming space, where search can be performed by hamming distance of compact hash codes. Depending upon minimum hamming distance it returns the similar image to query image. Keywords – Hamming distance, Hash codes, Hamming space, Hashing, Query adaptive image retrieval. I. Introduction The rapid evolution of multimedia and application has brought about an explosive growth of digital images in computer vision. This development has actually increase need for image retrieval system which is able to effectively index a massive amount of images and to efficiently retrieve them based on their visual contents. The term content based image retrieval (CBIR) appears to have been first used in the literature by Kato [1992] to describe his experiment in the automatic retrieval of images from database by colour and shape [1].In the past decade, many image retrieval system have been successfully developed, such as IBM QBIC system [2], developed at the IBM Almaden Research Centre, the Photobook system [3], developed my MIT Media Lab. There are two main approaches to image retrieval: text-based retrieval and content based retrieval [4]. The text-based approach requires a previous annotation of the database images, which is very lengthy and time consuming. Furthermore, the annotation process is inefficient because, users generally, do not create annotation in a proper way. Actually, different users tend to use different keywords to describe the same image characteristics. The lack of systemization in annotation process decreases the performance of text-based image retrieval. The alternative content-based method indexes images in database by identifying similarities between them based on low-level visual features as colour, texture, shape and spatial information. In this approach, it is possible to retrieve images similar to the image chosen by user. Advantage of this approach is the possibility of an automatic retrieval process, which reduces efforts required for annotating the image. The query adaptive image retrieval system performs two major tasks. The first task is feature extraction; here a set of features is extracted to describe the content of each image in the database. The second task is the similarity measurement between the query image and each image in the database, using the feature extraction. The feature extraction values for a given image are stored in a vector that can be used for retrieving similar images. Feature vector are descriptions of the visual features of the contents in images that produce such descriptions. They describe simple characteristics, such as colour, texture. The key to successful retrieval system is choosing the right feature to accurately represent images and the size of feature vector. This system uses low-level colour and texture feature. For colour feature extraction uses colour moment, autocorrelogram, colour histogram and for texture feature extraction uses gabor wavelet. The extracted features are embedding into hamming space using hashing technique. Hashing is preferable over tree-based indexing structure as it requires greatly reduced memory [5]. These extracted features are then embedded into hash codes for efficient search. Hash codes are used for similarity measure by using hamming distance. RESEARCH ARTICLE OPEN ACCESS