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.
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01 Jul 2003
TL;DR: A novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), to tackle the semantic gap problem and empirical results demonstrate the effectiveness of CLUE.
Abstract: "Semantic gap" is an open challenging problem in content-based image retrieval. It rejects the discrepancy between low-level imagery features used by the retrieval algorithm and high-level concepts required by system users. This paper introduces a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), to tackle the semantic gap problem. CLUE is built on a hypothesis that images of the same semantics tend to be clustered. It attempts to narrow the semantic gap by retrieving image clusters based on not only the feature similarity of images to the query, but also how images are similar to each other. CLUE has been tested using examples from a database of about 60,000 general-purpose images. Empirical results demonstrate the effectiveness of CLUE.
67 citations
TL;DR: A method that uses phylogenetic diversity indexes to characterize images for creating a model to classify histopathological breast images into four classes - invasive carcinoma, in situ carcinomas, normal tissue, and benign lesion is proposed.
67 citations
TL;DR: A bandelet transform based image representation technique is presented, which reliably returns the information about the major objects found in an image, which is evaluated on three standard data sets used in the domain of CBIR.
Abstract: One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.
67 citations
TL;DR: Experimental evaluation of ImageMiner shows that the system is able to support reliable detection and feature extraction of tumor regions within imaged tissues and the system was able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.
66 citations
01 Sep 2006-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: A new, effective system for content-based retrieval of figurative images, which is based on size functions, a geometrical-topological tool for shape description and matching, which outperforms other existing whole-image matching techniques, comprising features incorporated in the MPEG-7 standard.
Abstract: We propose a new, effective system for content-based retrieval of figurative images, which is based on size functions, a geometrical-topological tool for shape description and matching. Three different classes of shape descriptors are introduced and integrated, for a total amount of 25 measuring functions. The evaluation of our fully automatic retrieval system has been performed on a benchmark database of 10,745 real trademark images, supplied by the United Kingdom Patent Office. Comparative results show that our method actually outperforms other existing whole-image matching techniques, comprising features incorporated in the MPEG-7 standard.
66 citations