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Volume: 3 | Article ID: art00054
Content And Metadata Based Image Retrieval System For Art Images
  DOI :  10.2352/CGIV.2006.3.1.art00054  Published OnlineJanuary 2006

In this paper a new approach for image retrieval is analyzed using image content and metadata. Conventional content-based image retrieval systems are already in use but they are useful for some specific domains. This system provides an integrated approach with content and metadata. Pyramid wavelet transform is used for image decomposition and Daubechies family of filters is used for noise removal and filtering operations. The Euclidean distance classifier is used for finding the similarity measures between the query image and the images in the database. The same will also be used for sub-image matching. Calculating the low frequency band for the image has many advantages to reduce memory space, because all other higher frequency bands are eliminated. Clustered metadata is stored in various categories, which is used to form a query-by-text and to retrieve images. This clustered metadata is used to reduce searching time. All these methods are integrated to get higher performance. This image retrieval system is analyzed and simulated to show the performance analysis of the new approach

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Kingsly Stephen, Jency Moses, "Content And Metadata Based Image Retrieval System For Art Imagesin Proc. IS&T CGIV 2006 3rd European Conf. on Colour in Graphics, Imaging, and Vision,  2006,  pp 271 - 272,

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