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Content-based image retrieval of museum images

Content-based image retrieval of museum images
Content-based image retrieval of museum images
Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections.
Content-based image retrieval, texture analysis, texture segmentation, query by texture, query by low-quality image, wavelets
Ahmad Fauzi, Mohammad Faizal
4d6c2a94-8ca8-40c7-842d-d973efe9858b
Ahmad Fauzi, Mohammad Faizal
4d6c2a94-8ca8-40c7-842d-d973efe9858b

Ahmad Fauzi, Mohammad Faizal (2004) Content-based image retrieval of museum images. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections.

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Published date: 2004
Keywords: Content-based image retrieval, texture analysis, texture segmentation, query by texture, query by low-quality image, wavelets
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 261546
URI: https://eprints.soton.ac.uk/id/eprint/261546
PURE UUID: 8fc3255d-a25b-4721-ae69-ad2c061e24d1

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Date deposited: 14 Nov 2005
Last modified: 18 Jul 2017 09:01

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