This paper discusses the main aspects regarding the performance gap in Content-based Image Retrieval (CBIR) systems, which is an important issue regarding their acceptability. We also detail the main problems that lead to the performance gap: the use of many features to represent images, the lack of appropriate indexing structures for images and features, deficient query plans employed to execu...

In present world, the usage of internet and World Wide Web is rapidly increasing and due to which information storage and retrieval from databases become a important task. In this information images plays a great role. Therefore, the applications involving the search and management of digital images have gained interest of many researchers. Content-based image retrieval (CBIR) is a technique us...

the advancement in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. The success of such system largely depends upon the robustness, accuracy and speed of the retrieval systems. Content based image retrieval (CBIR) system is valuable in medical systems as it provides retrieval of ...

The notion of context is a key for problem solving in many domains, especially in Content-Based Image Retrieval (CBIR). However, one often considers the word context like words “concept” or “system”, i.e. without giving a clear definition. This problem has been encountered in artificial intelligence and fixed with a context model and a software called contextual graphs. In this paper, we point ...

With the increasing number of stereo images/videos in commercial markets, the demand for content-based image retrieval (CBIR) to deal with stereo media becomes urgent. To meet this demand, a novel framework is proposed where depth cues are extracted from stereo pairs and employed in a re-ranking scheme to refine results from conventional CBIR. Experiments show the proposed method yields promisi...

We explore the application of machine learning techniques to the problem of content-based image retrieval (CBIR). Unlike most existing CBIR systems in which only global information is used or in which a user must explicitly indicate what part of the image is of interest, we apply the multiple-instance (MI) learning model to use a small number of training images to learn what images from the dat...