Image re-ranking is one of the most efficient way by which you can improve the image search results and has been adopted by many search engines. Texture analysis is popular operation in CBIR. In this paper we have combined text based search along with CBIR and removing of duplicate images is done using pixel matching algorithm. Text based search is done using tags. CBIR is done using texture fe...

This chapter discusses key aspects concerning the performance of Content-based Image Retrieval (CBIR) systems. The so-called performance gap plays an important role regarding the acceptability of CBIR systems by the users. It provides a timely answer to the actual demand for computational support from CBIR systems that provide similarity queries processing. Focusing on the performance gap, this...

Content Based Image Retrieval (CBIR) system using Contourlet Transform (CT) based features with high retrieval rate and less computational complexity is proposed in this paper. Unique properties of CT like directionality and anisotropy made it a powerful tool for feature extraction of images in the database. Improved results in terms of computational complexity and retrieval efficiency are obse...

In Content-based Image Retrieval (CBIR) systems, accurately ranking collection images is of great relevance. Users are interested in the returned images placed at the first positions, which usually are the most relevant ones. Commonly, image content descriptors are used to compute ranked lists in CBIR systems. In general, these systems perform only pairwise image analysis, that is, compute simi...

The principles of content-based image retrieval (CBIR) are now well-established, with a continuing flow of publications exploring various aspects of the technology. Practical applications receive rather less attention and few fully-developed instances have been described. In this paper, we review the remarks made by other authors that led us to believe that it was important to investigate what ...

In today’s real life applications complexity of multimedia contents is significantly increased. This is highly demanding the development of effective retrieval systems to satisfy human desires. Recently, extensive research efforts have been carried out in the field of content-based image retrieval (CBIR). These research efforts are based on various parameters; feature extraction (to find conten...

The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates techn...

Content-based image retrieval (CBIR) is an emerging research field, studying retrieval of images from unannotated databases. In CBIR, images are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the gap between high-level semantic concepts and low-level visual features, the performance of CBIR applications often remains quite modest...

Content-based image retrieval (CBIR) is becoming an important field with the advance of multimedia and imaging technology everincreasingly. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. Content-based image retrieval can also be u...