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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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Proceedings ArticleDOI
23 Jun 2008
TL;DR: This paper proposes a novel scheme that exploits both semi-supervised kernel learning and batch mode active learning for relevance feedback in CBIR and shows that the proposed scheme is significantly more effective than other state-of-the-art approaches.
Abstract: Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to each other. In this paper, we propose a novel scheme that exploits both semi-supervised kernel learning and batch mode active learning for relevance feedback in CBIR. In particular, a kernel function is first learned from a mixture of labeled and unlabeled examples. The kernel will then be used to effectively identify the informative and diverse examples for active learning via a min-max framework. An empirical study with relevance feedback of CBIR showed that the proposed scheme is significantly more effective than other state-of-the-art approaches.

183 citations

Journal ArticleDOI
TL;DR: It is shown that BTC can not only be used for compressing color images, it can also be conveniently used for content-based image retrieval from image databases.
Abstract: This paper presents a new application of a well-studied image coding technique, namely block truncation coding (BTC). It is shown that BTC can not only be used for compressing color images, it can also be conveniently used for content-based image retrieval from image databases. From the BTC compressed stream (without performing decoding), we derive two image content description features, one termed the block color co-occurrence matrix (BCCM) and the other block pattern histogram (BPH). We use BCCM and BPH to compute the similarity measures of images for content-based image retrieval applications. Experimental results are presented which demonstrate that BCCM and BPH are comparable to similar state of the art techniques.

182 citations

Proceedings ArticleDOI
15 Sep 1999
TL;DR: A color-spatial method to include several spatial features of the colors in an image for retrieval, including area and position, which mean the zero-order and the first-order moments, respectively.
Abstract: Along with the analysis of color features in the hue, saturation and value (HSV) space, a new dividing method to quantize the color space into 36 non-uniform bins is introduced in this paper. Based on this quantization method we propose a color-spatial method to include several spatial features of the colors in an image for retrieval. These features are area and position, which mean the zero-order and the first-order moments, respectively. Experiments on an image database of 838 images show that the algorithm performs well in precision and adaptability.

181 citations

Journal ArticleDOI
TL;DR: This work presents a Genetic Programming framework that allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects.

180 citations

Journal ArticleDOI
TL;DR: A framework for computing low bit-rate feature descriptors with a 20× reduction in bit rate compared to state-of-the-art descriptors is proposed and it is shown how to efficiently compute distances between descriptors in the compressed domain eliminating the need for decoding.
Abstract: Establishing visual correspondences is an essential component of many computer vision problems, which is often done with local feature-descriptors Transmission and storage of these descriptors are of critical importance in the context of mobile visual search applications We propose a framework for computing low bit-rate feature descriptors with a 20× reduction in bit rate compared to state-of-the-art descriptors The framework offers low complexity and has significant speed-up in the matching stage We show how to efficiently compute distances between descriptors in the compressed domain eliminating the need for decoding We perform a comprehensive performance comparison with SIFT, SURF, BRIEF, MPEG-7 image signatures and other low bit-rate descriptors and show that our proposed CHoG descriptor outperforms existing schemes significantly over a wide range of bitrates We implement the descriptor in a mobile image retrieval system and for a database of 1 million CD, DVD and book covers, we achieve 96% retrieval accuracy using only 4 KB of data per query image

179 citations


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