Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm

Abstract

— In field of image processing and analysis Content-based image retrieval is a very important problem as there is rapid growth in storing and capturing multimedia data with digital devices. Although extensive studies, conducted and image finding is desired from multimedia databases and it is very challenging and open issue. This paper provides an review of the relevance feedback (RF), interactive genetic algorithm and neural network in content-based image retrieval (CBIR) . Relevance feedback enhance the capacity of CBIR effectively by reducing the semantic gap between low-level features and high levelfeatures. Interactive genetic algorithm is a branch of evolutionary computation which makes the retrieval process more interactive so that user can get refined results from database matching to Query Image with his evaluation . Neuro-fuzzy logic based implicit feedback get better results as compared to traditional implicit feedback. The paper covers the current achievements in relevance feedback , interactive genetic algorithm, neural network in CBIR, various relevance feedback techniques and applications of CBIR. Keywords— CBIR, Neuro-fuzzy logic, Relevance Feedback, Interactive Genetic Algorithm. I. INTRODUCTION A. Content Based Image Retrieval To diminish the lack of consistency problem, the image retrieval is carried out according to the image features. Such scheme is the so-called content-based image retrieval (CBIR). The main challenge of the CBIR system is to construct meaningful descriptions of physical attributes from images to expedite efficient and effective retrieval. CBIR has become an dynamic and fast-improving research area in image retrieval in the last few years. Due to this CBIR have improved in lots of way such as region-level features based, relevance feedback, semantic based etc. Content based features are mainly divided into two domains; Common visual features and Field Specific visual features like face recognition, task dependent applications etc. On the other hand, high level features include semantic based image retrieval computed from text description or by complex algorithms of visual features. The mixture of these content based features is required for better retrieval of image according to the application. Following are the some features of the image.