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K-NN regression to improve statistical feature extraction for texture retrieval

Khelifi, F and Jiang, J (2011) K-NN regression to improve statistical feature extraction for texture retrieval IEEE Transactions on Image Processing, 20 (1). pp. 293-298.

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Abstract

This correspondence presents an iterative method based upon -nearest neighbors (k-NN) regression to improve the performance of statistical feature extraction for texture image retrieval. The idea exploits the fact that an ideal feature extraction system would extract similar signatures from images characterized by the same texture and different signatures from dissimilar textures. Under the assumption that conventional statistical feature extraction contributes to sufficiently good retrieval performance, the signatures of k retrieved textures are used to update the signature of the query image using the k-NN regression algorithm. Extensive experiments show significant improvements with respect to retrieval performance in comparison to conventional statistical feature extraction.

Item Type: Article
Authors :
NameEmailORCID
Khelifi, FUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Date : 2011
Identification Number : 10.1109/TIP.2010.2052277
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:25
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835234

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