Navigational pattern based relevance feedback using user profile in CBIR

Content Based Image Retrieval (CBIR) is an application of computer vision and addresses the problem related to retrieval of digital images in large image databases. CBIR uses low level image features for retrieval task and tries to portray users intended results. Relevance Feedback (RF) is a technique for marking retrieved results as relevant or irrelevant by the user. People in the society have mutual interests and needs while searching for required data. Interesting and similar patterns can easily be found in the browsing behaviour of users pursuing required images from CBIR system. Recording users browsing behaviour and applying mining techniques to find frequent itemsets helps boost the retrieval performance of the CBIR system in terms of quality and processing time. User categorized into different groups on the basis of users age and gender specification helps fasten the mining process because of the similarity of thoughts in these users groups. This paper focuses on mining user browsing behaviour belonging to different user categories (user profiling) with FP-growth mining algorithm for revealing similar search patterns. The results show efficiency against the existing approach.