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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 novel approach for texture image retrieval is proposed by using a new set of two-dimensional rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly, which improves retrieval rate and retains comparable levels of computational complexity.

160 citations

Proceedings ArticleDOI
02 Nov 2003
TL;DR: The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
Abstract: Automatic annotation of photographs is one of the most desirable needs in family photograph management systems. In this paper, we present a learning framework to automate the face annotation in family photograph albums. Firstly, methodologies of content-based image retrieval and face recognition are seamlessly integrated to achieve automated annotation. Secondly, face annotation is formulated in a Bayesian framework, in which the face similarity measure is defined as maximum a posteriori (MAP) estimation. Thirdly, to deal with the missing features, marginal probability is used so that samples which have missing features are compared with those having the full feature set to ensure a non-biased decision. The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.

159 citations

Proceedings ArticleDOI
01 Jan 2003
TL;DR: A number of commonly used similarity measurements are described and evaluated in this paper and show that city block distance and /spl chi//sup 2/ Statistics measure outperform other distance measure in terms of both retrieval accuracy and retrieval efficiency.
Abstract: Similarity measurement is one of the key issues in content based image retrieval (CBIR). In CBIR, images are represented as features in the database. Once the features are extracted from the indexed images, the retrieval becomes the measurement of similarity between the features. Many similarity measurements exist. A number of commonly used similarity measurements are described and evaluated in this paper. They are evaluated in a standard shape image database. Results show that city block distance and /spl chi//sup 2/ Statistics measure outperform other distance measure in terms of both retrieval accuracy and retrieval efficiency.

158 citations

Proceedings ArticleDOI
01 Jan 2000
TL;DR: A salient point detector that extract points where variations occur in the image, whether they are corner-like or not, is presented, based on the wavelet transform to detect global variations as well as local ones.
Abstract: The use of interest points in content-based image retrieval allows the image index to represent local properties of the image. Classic corner detectors can be used for this purpose. However, they have drawbacks when applied to various natural images for image retrieval, because visual features need not be corners and corners may gather in small regions. We present a salient point detector that extract points where variations occur in the image, whether they are corner-like or not. The detector is based on the wavelet transform to detect global variations as well as local ones. The wavelet-based salient points are evaluated for image retrieval with a retrieval system using texture features. In this experiment our method provides better retrieval performance compared with other point detectors.

157 citations

Journal ArticleDOI
TL;DR: The biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features and shows a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery.
Abstract: With many potential multimedia applications, content-based image retrieval (CBIR) has recently gained more attention for image management and Web search. A wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user's preferences and bridge the semantic gap. However, there is still a big room to further the RF performance, because the popular RF algorithms ignore the manifold structure of image low-level visual features. In this paper, we propose the biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features. BDEE precisely models both the intraclass geometry and interclass discrimination and never meets the undersampled problem. To consider unlabelled samples, a manifold regularization-based item is introduced and combined with BDEE to form the semi-supervised BDEE, or semi-BDEE for short. To justify the effectiveness of the proposed BDEE and semi-BDEE, we compare them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery.

157 citations


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