Content-based image retrieval (CBIR) is a process of retrieving images based on their content in dataset automatically. CBIR common solution to search similar desired among all dataset. To do this, many methods have been developed extract features. Here, new Dominant Color Descriptor (DCD) method proposed improve accuracy. In the first step, Canny edges are extracted. next widened by employing ...

Content Based Image Retrieval (CBIR) is browsing, searching and navigation of images from large image databases based on their visual content. CBIR has been an active area of research for more than a decade. Many CBIR systems have been developed; like QBIC [Flickner et al. (1995)], Simplicity [Wang et al. (2001)], and Blob world [Carson et al.(2002)]. A detailed survey of CBIR techniques can be...

This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case content-based retrieval (CBIR). The main in compared to general CBIR, that subject whom query belongs may be affected by aging and progressive degenerative disorders, making it difficult match data on level. CBIR with DNNs generally solved minimizing ranking lo...

In a typical content-based image retrieval (CBIR) system, query result is a set of images sorted by feature similarities with respect to the query. We introduce a new approach to CBIR result representation. We propose that CBIR system should retrieve image clusters, which elements should be sorted by the most meaningful feature similarities. Actually, this paper does not present a full approach...

With the development of Internet technology and popularity digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed applied in various fields related to computer vision artificial intelligence. Currently, it is possible retrieve images effectively efficiently from a large-scale database with an input image. In past ten years, great efforts have made for new theories mode...

Content-Based Image Retrieval (CBIR) systems are required to effectively extract information from ubiquitous image collections. Retrieving images from a large and highly varied image data set based on their visual contents is extremely challenging. CBIR has been studied for decades and many good approaches have been proposed. But they do have some drawbacks. Texture and color are the significan...

Content Based Image Retrieval CBIR has become one of the most active research areas in the past few years Many visual feature representations have been explored 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 relatively ignored two distinct characteristics of CBIR systems the gap be...

In this paper we introduce and describe the Multimedia Retrieval Markup Language (MRML). This XML-based markup language is the basis for an open communication protocol for content-based image retrieval systems (CBIRSs). MRML was initially designed as a means of separating CBIR engines from their user interfaces. It is, however, also extensible as the basis for standardized performance evaluatio...

Content-based image retrieval (CBIR) is a new but widelyadopted method for finding images from vast and unannotated image databases. In CBIR images are indexed on the basis of low-level features, such as color, texture, and shape, that can automatically be derived from the visual content of the images. The operation of a CBIR system can be seen as a series of more or less independent processing...

As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also b...