1 This work was supported in part by NASA grant NAG5-12025 and by the National Science Foundation. The views expressed here are those of the authors and do not necessarily represent those of NASA or the National Science Foundation. ABSTRACT Content-based image retrieval (CBIR) uses features that can be extracted from the images themselves. In previous work we have shown that using more than one...

Content Based Image Retrieval (CBIR) presents special challenges in terms of how image data is indexed, accessed, and how end systems are evaluated. This paper discusses the design of a CBIR system that uses global colour as the primary indexing key, and a user centered evaluation of the system’s visual search tools. The results indicate that users are able to make use of a range of visual sear...

Digital images make up most multimedia data and are analysed in computer vision applications. Daily uploads of millions pictures to Internet archives such as satellite image repositories complicate content graphs. As feature vectors, based retrieval (CBIR) classification models represent high-level viewpoints. Observing photos recognizes objects evaluates their significance for enhancement. To ...

Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization the images. Different feature extraction technique have been proposed to overcome breach problems, however these methods suffer several shortcomings. This paper contributes retrieval system extract local features based on fus...

This paper presents the results of the University at Buffalo in the 2006 ImageCLEFmed task. Our approach for this task combines Content Based Image Retrieval (CBIR) and text retrieval to improve retrieval of medical images. Our results are comparable to other approaches presented in the task. Our results show that text retrieval performs well across the three different types of topics (visual, ...

Content Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extend from surveillance to remote sensing, medical imaging to weather forecasting, and security systems to historical research and so on. Though extensive research is made on content based image retriev...

Content Based Image Retrieval CBIR has be come one of the most active research areas in the past few years Many visual feature representations have been ex plored and many systems built While these research e orts establish the basis of CBIR the usefulness of the proposed approaches is limited Speci cally these e orts have rela tively ignored two distinct characteristics of CBIR systems the gap...

Content-based image retrieval (CBIR) has been proposed by the medical community for inclusion in picture archiving and communication systems (PACS). In CBIR, relevance feedback is developed for bridging the semantic gap and improving the effectiveness of image retrieval systems. With relevance feedback, CBIR systems can return refined search results using a learning algorithm and selection stra...

Increasing use of digital imaging processing leads to an enormous amount of imaging data. The access to picture archiving and communication systems (PACS), however, is solely textually, leading to sparse retrieval results because of ambiguous or missing image descriptions. Content-based image retrieval (CBIR) systems can improve the clinical diagnostic outcome significantly. However, current CB...

Study Design Preliminary evaluation of new tool. Objective To ascertain whether the newly developed content-based image retrieval (CBIR) software can be used successfully to retrieve images of similar cases of adolescent idiopathic scoliosis (AIS) from a database to help plan treatment without adhering to a classification scheme. Methods Sixty-two operated cases of AIS were entered into the new...