A Framework for Extensive Content-Based Image Retrieval System Incorporating Relevance Feedback and Query Suggestion

Over the last decade, there is a huge amount of images in which visual information has become increasingly important. If the images are to be utilized, they must be organized into databases, from there it can be searched based on different criteria. Content-based Image retrieval (CBIR) frameworks have turned into a mainstream subject of exploration; for their ability to retrieve images based on the real visual content rather than by manually connected textual descriptions. In the CBIR framework, analysis and interpretation of image information in large and diverse image databases is evidently complex because there is no prior information on the size or scale of individual structures within the images to be analyzed. In CBIR, retrieval is based on visual image features, which can be extracted automatically from the images with the help of human intervention, namely a technique called relevance feedback. Nonetheless, an efficient way of differentiating the visual content of images is complex to produce. Therefore, rather than a perfect solution CBIR systems must be able to exploit a partial solution to the problem of image understanding. In this paper, there is implementation of a CBIR framework is introduced that not only tries to efficiently capture the user intent based on the feedback but also provide query suggestions that can help its users to pose better queries in order to retrieve desired results in an efficient manner. The proposed technique is straightforward to implement and scopes efficiently to huge datasets. Extensive experiments on diverse real datasets with image similarity measures have revealed the dominance of the proposed method over original algorithms.