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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
Yihong Gong1
TL;DR: A novel system that strives to achieve advanced content-based image retrieval using seamless combination of two complementary approaches, which surpasses other methods under comparison in terms of not only quantitative measures, but also image retrieval capabilities.
Abstract: In this paper, we propose a novel system that strives to achieve advanced content-based image retrieval using seamless combination of two complementary approaches: on the one hand, we propose a new color-clustering method to better capture color properties of the original images; on the other hand, expecting that image regions acquired from the original images inevitably contain many errors, we make use of the available erroneous, ill-segmented image regions to accomplish the object-region-based image retrieval. We also propose an effective image-indexing scheme to facilitate fast and efficient image matching and retrieval. The carefully designed experimental evaluation shows that our proposed image retrieval system surpasses other methods under comparison in terms of not only quantitative measures, but also image retrieval capabilities.

35 citations

Journal ArticleDOI
TL;DR: An alternative image retrieval system based on the principle that it is the user who is most qualified to specify the query “content” and not the computer is presented, which was found to be superior to global indexing techniques as measured by statistical sampling of multiple users' “satisfaction” ratings.
Abstract: To date most “content-based image retrieval” (CBIR) techniques rely on global attributes such as color or texture histograms which tend to ignore the spatial composition of the image. In this paper, we present an alternative image retrieval system based on the principle that it is the user who is most qualified to specify the query “content” and not the computer. With our system, the user can select multiple “regions-of-interest” and can specify the relevance of their spatial layout in the retrieval process. We also derive similarity bounds on histogram distances for pruning the database search. This experimental system was found to be superior to global indexing techniques as measured by statistical sampling of multiple users' “satisfaction” ratings.

35 citations

Journal ArticleDOI
TL;DR: A region-level semantic mining approach where images are segmented into several parts using an improved segmentation algorithm, each with homogeneous spectral and textural characteristics, and then a uniform region-based representation for each image is built.
Abstract: As satellite images are widely used in a large number of applications in recent years, content-based image retrieval technique has become important tools for image exploration and information mining; however, their performances are limited by the semantic gap between low-level features and high-level concepts. To narrow this semantic gap, a region-level semantic mining approach is proposed in this article. Because it is easier for users to understand image content by region, images are segmented into several parts using an improved segmentation algorithm, each with homogeneous spectral and textural characteristics, and then a uniform region-based representation for each image is built. Once the probabilistic relationship among image, region, and hidden semantic is constructed, the Expectation Maximization method can be applied to mine the hidden semantic. We implement this approach on a dataset consisting of thousands of satellite images and obtain a high retrieval precision, as demonstrated through experiments.

35 citations

Journal ArticleDOI
TL;DR: The presented scheme has reduced the processing cost due to the consideration of a hierarchical approach and is suitable to handle mirror images during the retrieval process.
Abstract: Traditional Content-Based Image Retrieval (CBIR) systems were developed for retrieving similar kinds of images from a whole image database based on the given query image. In this paper, the authors have proposed a hierarchical approach for designing a CBIR scheme based on the color and texture features of an image. Initially, a color based approach is adopted and the intermediate results produced by using these color features is appropriate to discard a significant number of non-relevant images from the database. The intermediate database will be the input for the second stage. At this stage, a texture based approach is adopted for retrieving images from the intermediate database. The color features are extracted by computing the statistical parameters of non-uniform quantized histograms of HSV color space while a rotation invariant multi-resolution texture based approach is accomplished on value(V) component of HSV color space for extracting texture features. These texture features are extracted based on the principal texture direction and by taking the energies from various sub-bands of a dual tree complex wavelet transform (DT-CWT). Furthermore, the proposed scheme is suitable to handle mirror images during the retrieval process. The presented scheme has reduced the processing cost due to the consideration of a hierarchical approach. The proposed scheme is tested on the two well-known Corel-1K and GHIM-10K image databases respectively and satisfactory results were achieved in terms of precision, recall and F-score. The proposed scheme is compared with some other existing state of art CBIR schemes and the experimental results validate the improvement over other schemes in most of the instances.

35 citations

Proceedings ArticleDOI
Ke Gao1, Shouxun Lin1, Yongdong Zhang1, Sheng Tang1, Huamin Ren1 
14 May 2008
TL;DR: Experiments demonstrate that the attention model based SIFT keypoints filtration algorithm provides significant benefits both in retrieval accuracy and matching speed.
Abstract: Effective feature extraction is a fundamental component of content-based image retrieval. Scale Invariant Feature Transform (SIFT) has been proven to be the most robust local invariant feature descriptor. However, SIFT algorithm generates hundreds of thousands of keypoints per image, and most of them comes from background. This has seriously affected the application of SIFT in real-time image retrieval. This paper addresses this problem and proposes a novel method to filter the SIFT keypoints using attention model. Based on visual attention analysis, all of the keypoints in an image are ranked with their attention saliency, and only the most distinctive keypoints will be reserved. Then we use Bag of words to efficiently index these features. Experiments demonstrate that the attention model based SIFT keypoints filtration algorithm provides significant benefits both in retrieval accuracy and matching speed.

35 citations


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