Content based image retrieval (CBIR) has been possibly the greatest significant enquiry areas in computer science for the last decade. A retrieval way which mix texture, color and shape feature is future in this paper. In this research, implemented a novel method for CBIR using Hough Transform ,DCD and DWT feature with Support vector machine (SVM) as a classifier. In the process of feature extr...

Content-based image retrieval (CBIR) selects, from a repository, those images whose content matches a query. In current approaches queries can be example pictures, object descriptions, or picture types. The answer contains the pictures which are similar to the given example, or that contain the described object, or which is of the specified type. Semantic CBIR allows queries to be complex expre...

Most of the approaches of Content-Based Image Retrieval (CBIR) presume a linear relationship between different image features, and the efficiency of such systems was limited due to the difficulty in representing high-level concepts using low-level features. In this paper, a new architecture for a CBIR system is proposed; the Splines Neural Network-based Image Retrieval (SNNIR) system. SNNIR mak...

In Internet, Multimedia and Image Databases image searching is a necessity. Content-Based Image Retrieval (CBIR) is an approach for image retrieval. With User interaction included in CBIR with Relevance Feedback (RF) techniques, the results are obtained by giving more number of iterative feedbacks for large databases is not an efficient method for realtime applications. So, we propose a new app...

In this paper, we reviewed different methods used in Content-based image retrieval, to resolve the problem of efficient and similar digital image retrieval from large databases with high precision. Uses of different features like texture, color, shape have been focused in different ways to implement better and faster retrieval of data through different CBIR methods. Methods like Statistical Fra...

Supervised learning algorithms (relevance feedback (RF) algorithms) are often used in content based image retrieval (CBIR) systems to enhance interactive search and browsing of image databases. One of the issues associated with RF based CBIR systems is the lack of a large training set. Labeling of images is a time consuming activity and user’s usually do not have the patience to label a large s...

Content-based image retrieval (CBIR) systems can be used also for other purposes than online access to unannotated image databases. In particular, when a CBIR system is equipped with an automatic image segmentation subsystem, keyword annotations given on image level can be focused on specific image segments. In this paper, we show that our PicSOM CBIR system is able to reveal semantic knowledge...

As an interesting application on cloud computing, content-based image retrieval (CBIR) has attracted a lot of attention, but the focus of previous research work was mainly on improving the retrieval performance rather than addressing security issues such as copyrights and user privacy. With an increase of security attacks in the computer networks, these security issues become critical for CBIR ...

In content-based image retrieval (CBIR), the images in a database are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the semantic gap, the performance of CBIR systems often remains quite modest especially on broad image domains. One method for improving the results is to incorporate automatic image classification methods to the C...

Content-based image retrieval (CBIR) is a new but in recent years widely-adopted method for nding images from vast and unanno-tated image databases. CBIR is a technique for querying images on the basis of automatically-derived features such as color, texture, and shape directly from the visual content of images. For the development of eeec-tive image retrieval applications, one of the most urge...