Multimedia Systems and Content-Based Image Retrieval


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Content‐based image retrieval based on binary signatures cluster graph

Google Scholar. Hellerstein, and Jitendra Malik. Blobworld: A system for region-based image indexing and retrieval. In Huijsmans and Smeulders [8].

John P. Eakins and Margaret E. Content-based image retrieval, a report to the JISC technology application programme. David A. Forsyth and Margaret M. Body plans.

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CrossRef Google Scholar. Theo Gevers and Arnold Smeulders. Pictoseek: Combining color and shape invariant features for image retrieval. Multimedia information systems.

IEEE Multimedia , 1 1 : , Gudivada and V. Content-based image retrieval systems. Huijsmans and A.

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Survey on Content Based Image Retrieval Systems | Open Access Journals

Smeulders, editors. Springer, Kreyss, M. Alshuth, Th.

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Hermes, and O. Video retrieval by still image analysis with ImageMiner.

Michael S. Lew, D. Huijsmans, and Dee Denteneer. Content based image retrieval: KLT, projections, or templates. Amsterdam University Press, August Li, O. Illumination invariance and object model in content-based image and video retrieval.

Content-based image retrieval

Wei-Ying Ma and B. Netra: A toolbox for navigating large image databases. Multimedia Systems , 7 3 : —, Manmatha and S. A syntactic characterization of appearance and its application to image retrieval. Surfimage: A flexible content-based image retrieval system. Niblack, R. Barber, W. Equitz, M.

Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Combining CBIR search techniques available with the wide range of potential users and their intent can be a difficult task. An aspect of making CBIR successful relies entirely on the ability to understand the user intent. Examples of this type of interface have been developed. Machine learning and application of iterative techniques are becoming more common in CBIR. The most common method for comparing two images in content-based image retrieval typically an example image and an image from the database is using an image distance measure.

An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images.


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Search results then can be sorted based on their distance to the queried image. Computing distance measures based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values. Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located.

Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation.

Simone Santini, Ramesh Jain

The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated. Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying segmentation or edge detection to an image. Other methods use shape filters to identify given shapes of an image.

Some shape descriptors include: [5]. Measures of image retrieval can be defined in terms of precision and recall. However, there are other methods being considered. An image is retrieved in CBIR system by adopting several techniques simultaneously such as Integrating Pixel Cluster Indexing, histogram intersection and discrete wavelet transform methods.

Potential uses for CBIR include: [2]. Commercial Systems that have been developed include: [2]. Experimental Systems include: [2]. From Wikipedia, the free encyclopedia. See also: Reverse image search. University of Northumbria at Newcastle.

Multimedia Systems and Content-Based Image Retrieval

Archived from the original on Retrieved Abstract: Research on ways to extend and improve query methods for image databases is widespread. Journal of Visual Communication and Image Representation. Scientific Reports.


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Bibcode : NatSR QBE is a language for querying Computer Vision. Wang ACM Computing Surveys. Elliott, Griffiths

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Multimedia Systems and Content-Based Image Retrieval Multimedia Systems and Content-Based Image Retrieval
Multimedia Systems and Content-Based Image Retrieval

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