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Preprocessing for content-based image retrieval

Preprocessing for content-based image retrieval
Preprocessing for content-based image retrieval
The research focuses on image retrieval problems where the query is formed as an image of a specific object of interest. The broad aim is to investigate pre-processing for retrieval of images of objects when an example image containing the object is given. The object may be against a variety of backgrounds. Given the assumption that the object of interest is fairly centrally located in the image, the normalized cut segmentation and region growing segmentation are investigated to segment the object from the background but with limited success. An alternative approach comes from identifying salient regions in the image and extracting local features as a representation of the regions. The experiments show an improvement for retrieval by local features when compared with retrieval using global features from the whole image.

For situations where object retrieval is required and where the foreground and background can be assumed to have different characteristics, it is useful to exclude salient regions which are characteristic of the background if they can be identified before matching is undertaken. This thesis proposes techniques to filter out salient regions believed to be associated with the background area. Background filtering using background clusters is the first technique which is proposed in the situation where only the background information is available for training. The second technique is the K-NN classification based on the foreground and background probability. In the last chapter, the support vector machine (SVM) method with PCA-SIFT descriptors is applied in an attempt to improve classification into foreground and background salient region classes. Retrieval comparisons show that the use of salient region background filtering gives an improvement in performance when compared with the unfiltered method.
content-based image retrieval, image segmentation, salient regions, background filtering, background/foreground Classification
Rodhetbhai, Wasara
47577f04-d0da-4d19-aa24-f0ce0f120862
Rodhetbhai, Wasara
47577f04-d0da-4d19-aa24-f0ce0f120862
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020

Rodhetbhai, Wasara (2009) Preprocessing for content-based image retrieval. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 128pp.

Record type: Thesis (Doctoral)

Abstract

The research focuses on image retrieval problems where the query is formed as an image of a specific object of interest. The broad aim is to investigate pre-processing for retrieval of images of objects when an example image containing the object is given. The object may be against a variety of backgrounds. Given the assumption that the object of interest is fairly centrally located in the image, the normalized cut segmentation and region growing segmentation are investigated to segment the object from the background but with limited success. An alternative approach comes from identifying salient regions in the image and extracting local features as a representation of the regions. The experiments show an improvement for retrieval by local features when compared with retrieval using global features from the whole image.

For situations where object retrieval is required and where the foreground and background can be assumed to have different characteristics, it is useful to exclude salient regions which are characteristic of the background if they can be identified before matching is undertaken. This thesis proposes techniques to filter out salient regions believed to be associated with the background area. Background filtering using background clusters is the first technique which is proposed in the situation where only the background information is available for training. The second technique is the K-NN classification based on the foreground and background probability. In the last chapter, the support vector machine (SVM) method with PCA-SIFT descriptors is applied in an attempt to improve classification into foreground and background salient region classes. Retrieval comparisons show that the use of salient region background filtering gives an improvement in performance when compared with the unfiltered method.

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More information

Published date: May 2009
Keywords: content-based image retrieval, image segmentation, salient regions, background filtering, background/foreground Classification
Organisations: University of Southampton

Identifiers

Local EPrints ID: 66393
URI: http://eprints.soton.ac.uk/id/eprint/66393
PURE UUID: bf51853c-56c6-46e8-8deb-3afbacb3f7c6

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Date deposited: 10 Jun 2009
Last modified: 08 Jan 2022 23:46

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Contributors

Author: Wasara Rodhetbhai
Thesis advisor: Paul Lewis

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