Topic
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 published on a yearly basis
Papers
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TL;DR: In this article, an approach to learn feature weights for content-based image retrieval (CBIR) from user interaction log files is described and a significant improvement in performance is reached with a feature weighting calculated from the information contained in the log files.
Abstract: This article describes an approach to learn feature weights for content-based image retrieval (CBIR) from user interaction log files. These usage log files are analyzed for images marked together by a user in the same query step. The problem is somewhat similar to one of the traditional data mining problems, the market basket analysis problem, where items bought together in a supermarket are analyzed. This paper outlines similarities and differences between the two fields and explains how to use the interaction data for deriving a better feature weighting.
Experiments with existing log files are done and a significant improvement in performance is reached with a feature weighting calculated from the information contained in the log files. Even with several steps of relevance feedback the results remain much better than without the learning, which means that not only information from feedback is taken into account earlier, but a better quality of retrieval is reached in all steps.
65 citations
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TL;DR: The qualitative and quantitative analysis performed on three image collections shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.
Abstract: For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.
65 citations
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25 Sep 2008TL;DR: This paper proposed a novel approach to retrieve images by texture characterization using a composition of edge information and co-occurrence matrix properties, which gives encouraging results when comparing its retrieval performance to that of the traditional co- Occurrence matrices and Yaopsilas approach.
Abstract: Content-Based Image Retrieval (CBIR) system is emerging as an important research area, users can search and retrieve images based on their properties such as shape, color and texture from the image database. Usually texture-based image retrieval just consider an original image of coarseness, contrast and roughness, actually there is much texture information in the edge image. This paper proposed a novel approach to retrieve images by texture characterization using a composition of edge information and co-occurrence matrix properties. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional co-occurrence matrices and Yaopsilas approach.
65 citations
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TL;DR: This article proposes a shape salience detector and a shape descriptor-Tensor Scale Descriptor with Influence Zones and introduces a robust method to compute tensor scale, using a graph-based approach-the Image Foresting Transform.
64 citations
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TL;DR: Local Oppugnant Color Texture Pattern (LOCTP) is proposed, an enhancement of LTrP, which is able to discriminate the information derived from spatial inter-chromatic texture patterns of different spectral channels within a region.
64 citations