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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
TL;DR: A new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the query in order to accurately represent the user's particular information needs.
Abstract: Content-based image retrieval systems require the development of relevance feedback mechanisms that allow the user to progressively refine the system's response to a query. In this paper a new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the query in order to accurately represent the user's particular information needs. Experimental results demonstrate the effectiveness of this mechanism.

107 citations

Journal ArticleDOI
TL;DR: An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter, which paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.
Abstract: Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.

107 citations

Journal ArticleDOI
TL;DR: A model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method to retain the hierarchical relationship of the regions in an image during segmentation is proposed.
Abstract: In this work, we propose a model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method. We retain the hierarchical relationship of the regions in an image during segmentation. Using the information of the relationships and features of the regions, we can represent the desired objects in images more accurately. In retrieval, we compare not only region features but also region relationships.

107 citations

Journal ArticleDOI
TL;DR: A journey through the main information fusion ingredients that a recipe for the design of a CBIR system should include to meet the demanding needs of users is offered.

106 citations

Journal ArticleDOI
TL;DR: This article focuses on the topic of content-based image retrieval using interactive search techniques, i.e., how does one interactively find any kind of imagery from any source, regardless of whether it is photographic, MRI or X-ray?
Abstract: We are living in an Age of Information where the amount of accessible data from science and culture is almost limitless. However, this also means that finding an item of interest is increasingly difficult, a digital needle in the prover- bial haystack. In this article, we focus on the topic of content- based image retrieval using interactive search techniques, i.e., how does one interactively find any kind of imagery from any source, regardless of whether it is photographic, MRI or X-ray? We highlight trends and ideas from over 170 recent research papers aiming to capture the wide spec- trum of paradigms and methods in interactive search, includ- ing its subarea relevance feedback. Furthermore, we identify promising research directions and several grand challenges for the future.

106 citations


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