K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval

Khelifi, Fouad and Jiang, Jianmin (2011) K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20 (1). pp. 293-298. ISSN 1057-7149

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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
Uncontrolled Keywords:	NN regression algorithm, iterative method, nearest neighbors regression, query image, retrieval performance, retrieved textures, statistical feature extraction, texture image retrieval
Subjects:	G400 Computer Science G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Ay Okpokam
Date Deposited:	23 Dec 2011 13:34
Last Modified:	13 Oct 2019 00:31
URI:	http://nrl.northumbria.ac.uk/id/eprint/4420

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