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Localized content based image retrieval

Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, 2005
We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, Accio, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and ...
Rouhollah, Rahmani   +4 more
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Faceted Content-Based Image Retrieval

2008 19th International Conference on Database and Expert Systems Applications, 2008
In typical content-based image retrieval systems it is not possible to navigate the image space by simultaneously applying multiple similarity criteria. The model we propose addresses this problem by representing the search for the images similar to a given image as the exploration of a lattice of (non-disjoint) image clusters, induced by a natural ...
Amato G, Meghini C
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Content-Based Image Retrieval

2009
Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without the help of annotations. The current CBIR systems use global features (e.g., color, texture, and shape) as image descriptors, or usefeatures extracted from segmented regions (called region-based descriptors).
Ming Zhang, Reda Alhajj
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Content-based image retrieval

Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, 2005
The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image ...
Ritendra Datta, Jia Li, James Z. Wang
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Content-based image retrieval

2008 6th International Symposium on Intelligent Systems and Informatics, 2008
A picture is worth a thousand words. Yes, but which ones? Content-based image retrieval (CBIR) is the application of computer vision to the image retrieval problem. The image retrieval problem is the problem of searching for digital images in large databases.
Igor Marinovic, Igor Furstner
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Content-Based Image Retrieval in Astronomy

Information Retrieval, 2000
Summary: Content-based image retrieval in astronomy needs methods that can deal with an image content made of noisy and diffuse structures. This motivates investigations on how information should be summarized and indexed for this specific kind of images.
Csillaghy, A.   +2 more
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Content-Based Image Retrieval

1995
As more and more image data are acquired and assume the role of “first-class citizens” in information technology, managing and manipulating them as images becomes an important issue to be resolved before we can take full advantage of their information content [CH92]. Image database and visual information system technologies have become major efforts to
Borko Furht   +2 more
openaire   +1 more source

Content-based image retrieval

SPIE Proceedings, 2010
Large collection of information is being created in many areas of modern life on daily basis. This information exists in many forms from plain text to high resolution multimedia. Today computers are many times faster than human in text based searching using keywords and indexing but the story is totally different in case of multimedia.
openaire   +1 more source

Content-based image retrieval for medical infrared images

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005
Past efforts on the automated processing on medical infrared images has typically focused on specialized applications like the detection of breast cancer. We propose the application of content-based image retrieval (CBIR) to medical thermal images. CBIR allows the retrieval of similar images based on features directly extracted from the image data ...
B F, Jones, G, Schaefer, S Y, Zhu
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