(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 8, 2013 42 | Page www.ijacsa.thesai.org User-Based Interaction for Content-Based Image Retrieval by Mining User Navigation Patterns. A.Srinagesh 1 CSE Department, RVR & JC College of Engineering Guntur-522019. India G.P.Saradhi Varma 2 IT Department, SRKR College of Engineering Bhimavaram-534204, India Lavanya Thota 1 CSE Department, RVR & JC College of Engineering Guntur-522019. India. A.Govardhan 3 CSE Department, JNTUH Hyderabad-500085. India Abstract—In Internet, Multimedia and Image Databases image searching is a necessity. Content-Based Image Retrieval (CBIR) is an approach for image retrieval. With User interaction included in CBIR with Relevance Feedback (RF) techniques, the results are obtained by giving more number of iterative feedbacks for large databases is not an efficient method for real- time applications. So, we propose a new approach which converges rapidly and can aptly be called as Navigation Pattern- Based Relevance Feedback (NPRF) with User-based interaction mode. We combined NPRF with RF techniques with three concepts viz., query Re-weighting (QR), Query Expansion (QEX) and Query Point Movement (QPM). By using, these three techniques efficient results are obtained by giving a small number of feedbacks. The efficiency of the proposed method with results is proved by calculating Precision, Recall and Evaluation measures. Keywords—Image Retrieval; CBIR; Relevance Feedback; Navigation Patterns; Query Expansion; Query Reweighting; Query Point Movement. I. INTRODUCTION The popularity of an image retrieval system plays an important role in it’s usage and application which, in turn is dependent on it’s implementation. Image retrieval systems such as CBIR take a great challenge of retrieving images from a large database. Everywhere, we see the usage of images and image retrieval technique plays a vital role in different application areas like for ex: Medical Diagnosis, Military, Retail catalogs etc. There are many traditional approaches for information retrieval, but they can’t satisfy the user’s need to retrieve images upto a satisfactory level. CBIR techniques were firstly introduced by Rui, Hunag, and Chang, in late 1990s. There are some CBIR techniques which are search or retrieval type with browsing as a major mode of querying. CBIR is based on navigating an image collection of size 35,000 along conceptual dimensions that describes images in the collection is a very much useful method. It can also be used for intelligent image retrieval and browsing using semantic web-based techniques. All systems introduced for automatically classifying images gathered on the Web are based on the CBIR system. Another example system is art image retrieval based on user profiles is developed and it uses probabilistic support vector machines (SVM) to model user profiles. The same method is presented for automatic image annotation using cross media relevance models. Current interfaces of CBIR system describes an alternative interface based on a study of how home users use traditional ways of storing and organizing personal photo collections, but leveraging new possibilities enabled by digital media is not attempted. Some of researchers proposed approaches related to CBIR that involves multiple sources of information like text, HTML tags which are required to search for the images. Several scenarios exist where medical practitioners can benefit from the use of these types of relevance feedback systems. Feedback functionality is to be provided for radiologists in assessing medical images, which is used in medical diagnosis. It is also useful as a clinical tool or in an academic context where students can benefit from access to similar diagnosed data. Content-based access to medical images has strong impacts for computer-aided diagnosis, evidence-based medicine. For each application, a certain GUI is composed and connected to the IRMA core hosting the database as well as the programs for feature extraction and comparison. However, several mechanisms are of major importance in every image retrieval system. Feature extraction [12] is one of the ways to retrieve an image. Feature extraction plays a major role in CBIR systems. Mapping the image pixels into the feature space is feature extraction. By using this extracted feature we can search, index and browse the image from the stored database. This feature can be used to measure the similarity between the stored images. Image retrieval approaches are based on the computing the similarity between the input query image and database images via Query by Example (QBE) system [9]. The problem occurred in this is extracted visual features are too diverse to be captured with the concept of query given by user. To, solve such problem in QBE system, user need to provide feedback like pick relevant images from retrieval of images iteratively, the feedback procedure is called Relevance Feedback (RF). This feedback is given up to user satisfaction with the retrieval results. To solve problems we propose an approach called Navigation-Pattern-Based Relevance Feedback (NPRF) which