ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 1, Issue 6, August 2012 100 All Rights Reserved © 2012 IJARCET Memory Learning Framework for Retrieval of Neural Objects Sanjeev S. Sannakki 1 , Sanjeev P. Kaulgud 2 1 Computer Science and Engineering, Gogte Institute of Technology, Belgaum 2 Department of PG Studies, Visvesvaraya Technological University, Belgaum Belgaum, Karnataka, India. Abstract: Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of large number of images demonstrate the effectiveness of the proposed framework. Keywords— CBIR, Image Retrieval, Relevance Feedback, Image authoritative rank, Memory Learning Framework, Feature Extraction I. INTRODUCTION ―Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases‖. Image retrieval is the process of browsing, searching and retrieving images from a large database of digital images. The collection of images in the web are growing larger and becoming more diverse .Retrieving images from such large collections is a challenging problem. One of the main problems they highlighted was the difficulty of locating a desired image in a large and varied collection. While it is perfectly possible to identify a desired image from a small collection simply by browsing, more effective techniques are needed with collections containing thousands of items. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images "similar" to the query. The similarity used for search criteria could be Meta tags, color distribution in images, etc. Unfortunately, image retrieval systems have not kept pace with the collections they are searching. The shortcomings of these systems are due both to the image representations they use and to their methods of accessing those representations to find images. A. Overview of Content Based Image Retrieval In recent years, with large scale storing of images the need to have an efficient method of image searching and retrieval has increased. It can simplify many tasks in many application areas such as biomedicine, forensics, artificial intelligence, military, education, web image searching. Most of the image retrieval systems present today are text-based, in which images are manually annotated by text-based keywords and when we query by a keyword, instead of looking into the contents of the image, this system matches the query to the keywords present in the database. An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Earlier Image searching done by the technique called Image Meta search. Image Meta search is a searching technique that searches the images based on the Image Meta data such as text, keywords etc. Due to the rapidly growing amount of digital image data on the Internet and in digital libraries, there is a great need for large image database management and effective image retrieval tools. Content-based image retrieval (CBIR) is the set of techniques for searching for similar images from an image database using automatically extracted image features. Tremendous research has been devoted to CBIR and a variety of solutions have been proposed within the past ten years. By and large, research activities in CBIR have progressed in three major directions:  Global features based.  Object/region-level features based.  Relevance feedback. All web search engines leaders, such as Google, Yahoo, Ask and etc., find multimedia content by means of text descriptions. Billions of images are tagged manually by