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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.


Papers
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Journal ArticleDOI
17 Jun 2014
TL;DR: This paper presents a novel, fast and effective hybrid framework for color image retrieval through combination of all the low level features, which gives higher retrieval accuracy than other such systems.
Abstract: This paper presents a novel, fast and effective hybrid framework for color image retrieval through combination of all the low level features, which gives higher retrieval accuracy than other such systems. The color moment (CMs), angular radial transform descriptor and edge histogram descriptor (EHD) features are exploited to capture color, shape and texture information respectively. A multistage framework is designed to imitate human perception so that in the first stage, images are retrieved based on their CMs and then the shape and texture descriptors are utilized to identify the closest matches in the second stage. The scheme employs division of images into non-overlapping regions for effective computation of CMs and EHD features. To demonstrate the efficacy of this framework, experiments are conducted on Wang’s, VisTex and OT-Scene databases. Inspite of its multistage design, the system is observed to be faster than other hybrid approaches.

27 citations

Journal ArticleDOI
TL;DR: An effective block-oriented image decomposition structure which can be used to represent image content in image database systems is introduced and the application of this image data model to content-based image retrieval is discussed.
Abstract: In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We first introduce an effective block-oriented image decomposition structure which can be used to represent image content in image database systems. We then discuss the application of this image data model to content-based image retrieval. Using wavelet transforms to extract image features, significant content features can be extracted from image data through decorrelating the data in their pixel format into frequency domain. Feature vectors of images can then be constructed. Content-based image retrieval is performed by comparing the feature vectors of the query image and the decomposed segments in database images. Our experimental analysis illustrates that the proposed block-oriented image representation offers a novel decomposition structure to be used to facilitate effective and efficient image retrieval.

27 citations

Journal ArticleDOI
TL;DR: This paper presents an approach to represent spatial colour distributions using local principal component analysis (PCA), based on image windows which are selected by two complementary data driven attentive mechanisms: a symmetry based saliency map and an edge and corner detector.

27 citations

Proceedings ArticleDOI
18 Apr 2011
TL;DR: This paper presents a novel re-ranking approach based on contextual spaces aiming to improve the effectiveness of CBIR tasks, by exploring relations among images.
Abstract: The objective of Content-based Image Retrieval (CBIR) systems is to return the most similar images given an image query. In this scenario, accurately ranking collection images is of great relevance. In general, CBIR systems consider only pairwise image analysis, that is, compute similarity measures considering only pair of images, ignoring the rich information encoded in the relations among several images. This paper presents a novel re-ranking approach based on contextual spaces aiming to improve the effectiveness of CBIR tasks, by exploring relations among images. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information. The re-ranking method can also be applied to other tasks, such as: (i) for combining ranked lists obtained by using different image descriptors (rank aggregation); and (ii) for combining post-processing methods. We conducted several experiments involving shape, color, and texture descriptors and comparisons to other post-processing methods. Experimental results demonstrate the effectiveness of our method.

27 citations

Proceedings ArticleDOI
06 Mar 2008
TL;DR: A prototype system implemented for CBIR for a uterine cervix image (cervigram) database is presented, which tries to bridge the gap between a user's semantic understanding and image feature representation, by incorporating the user's knowledge.
Abstract: Content-based image retrieval (CBIR) is th e process of retrieving images by dir ectly using image visual characteristics. In this paper, we present a prototype system implemented for CBIR for a uterine cervix image (cervigram) database. This cervigram database is a part of data collected in a multi-year longitudinal effort by the National Cancer Institute (NCI), and archived by the National Library of Medicine (NLM), for the study of the origins of, and factors related to, cervical precancer/cancer. Users may access the system with any Web browser. The system is built with a distributed architecture which is modular and expandable; the user interface is decoupled from the core indexing and retrieving algorithms, and uses open communication standards and open source software. The system tries to bridge the gap between a user’s semantic understanding and image feature representation, by incorporating the user’s knowledge. Given a user-specified query region, the system returns the most similar regions from the database, with respect to attributes of color, texture, and size. Experimental evaluation of the retrieval performance of the system on “ground-truth” test data illustrates its feasibility to serve as a possibl e research tool to aid the study of the visual characteristic s of cervical neoplasia. Keywords: content-based image retrieval, Web-based me dical image system, uterine cervix cancer

27 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202358
2022141
2021180
2020163
2019224
2018270