Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Fuzzy hamming distance in a content-based image retrieval system

Abstract: The performance of content-based image retrieval (CBIR) systems mainly depends on the image similarity measure that it uses. The fuzzy Hamming distance (D) is an extension of the Hamming distance for real-valued vectors. Because the feature space of each image is real-valued, the fuzzy Hamming distance can be successfully used as an image similarity measure. The current study reports on the results of applying D as a similarity measure between the color histograms of two images. The fuzzy Hamming distance is suitable for this application because it can take into account not only the number of different colors but also the magnitude of this difference.

Content Based Image Retrieval by combining color, texture and CENTRIST

Abstract: This paper presents a novel framework for Content Based Image Retrieval(CBIR), which combines color, texture and spatial structure of image. The proposed method uses color, texture and spatial structure descriptors to form a feature vector. Images are segmented into regions to extract local color, texture and CENTRIST(CENsus Transform hISTogram) features respectively. Multiple-instance learning (MIL) and Diverse Density(DD) are incorporated with regions as instances to find the objective instance. In addition, to denote the whole structure of image better, we perform PCA to CENTRIST features of all images, i.e. spatial Principal component Analysis of Census Transform(spatial PACT). This framework integrates three features to enhance the retrieval performance. Experiments on COREL standard database invalidate the proposed method by comparing with some state-of-the-art methods. (4 pages)

Content-based image retrieval via a hierarchical-local-feature extraction scheme

Abstract: Recently, with the development of various camera sensors and internet network, the volume of digital images is becoming big. Content-based image retrieval (CBIR), especially in network big data analysis, has attracted wide attention. CBIR systems normally search the most similar images to the given query example among a wide range of candidate images. However, human psychology suggests that users concern more about regions of their interest and merely want to retrieve images containing relevant regions, while ignoring irrelevant image areas (such as the texture regions or background). Previous CBIR system on user-interested image retrieval generally requires complicated segmentation of the region from the background. In this paper, we propose a novel hierarchical-local-feature extraction scheme for CBIR, whereas complex image segmentation is avoided. In our CBIR system, a perception-based directional patch extraction method and an improved salient patch detection algorithm are proposed for local features extraction. Then, color moments and Gabor texture features are employed to index the salient regions. Extensive experiments have been performed to evaluate the performance of the proposed scheme, and experimental results show that the developed CBIR system produces plausible retrieval results.

Shape-Based Image Retrieval Using Combining Global and Local Shape Features

Abstract: Content-based image retrieval (CBIR) has been an active research topic in the last decade. Using just one kind of feature information may cause inaccuracy compared with using more than two kinds of feature information. Aiming at shape- based image retrieval, in this paper we proposed an image retrieval method using the global and local shape features. Firstly, an image is segmented, and then the Compactness and Fourier Descriptor as local features are extracted. In order to remedy the effect of image segmentation on feature description and improve retrieval performance, global feature is extracted by Krawtchouk moment invariants. Finally, this approach uses the combined local and global shape features as feature vectors to achieve image retrieval. Experiments have been conducted on a database consisting of 500 images, compared with the method of using local shape features, experiments results show that this approach is more effective in image retrieval and improves the accuracy.

An Attention-Based Approach to Content-Based Image Retrieval

Abstract: Mark Weiser's vision that ubiquitous computing will overcome the problem of information overload by embedding computation in the environment is on the verge of becoming a reality. Nevertheless today's technology is now capable of handling many different forms of multimedia that pervade our lives and as a result is creating a healthy demand for new content management and retrieval services. This demand is everywhere; it is coming from the mobile videophone owners, the digital camera owners, the entertainment industry, medicine, surveillance, the military, and virtually every library and museum in the world where multimedia assets are lying unknown, unseen and unused. The volume of visual data in the world is increasing exponentially through the use of digital camcorders and cameras in the mass market. These are the modern day consumer equivalents of ubiquitous computers, and, although storage space is in plentiful supply, access and retrieval remain a severe bottle-neck both for the home user and for industry. This paper describes an approach, which makes use of a visual attention model together with a similarity measure, to automatically identify salient visual material and generate searchable metadata that associates related items in a database. Such a system for content classification and access will be of great use in current and future pervasive environments where static and mobile content retrieval of visual imagery is required.