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Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise

Albukhanajer, WA, Briffa, JA and Jin, YAOCHU (2014) Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise IEEE Transactions on Cybernetics, 45 (9). pp. 1757-1768.

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Abstract

A Pareto-based evolutionary multiobjective approach is adopted to optimize the functionals in the trace transform (TT) for extracting image features that are robust to noise and invariant to geometric deformations such as rotation, scale, and translation (RST). To this end, sample images with noise and with RST distortion are employed in the evolutionary optimization of the TT, which is termed evolutionary TT with noise (ETTN). Experimental studies on a fish image database and the Columbia COIL-20 image database show that the ETTN optimized on a few low-resolution images from the fish database can extract robust and RST invariant features from the standard images in the fish database as well as in the COIL-20 database. These results demonstrate that the proposed ETTN is very promising in that it is computationally efficient, invariant to RST deformation, robust to noise, and generalizable.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Surrey Space Centre
Authors :
AuthorsEmailORCID
Albukhanajer, WAUNSPECIFIEDUNSPECIFIED
Briffa, JAUNSPECIFIEDUNSPECIFIED
Jin, YAOCHUUNSPECIFIEDUNSPECIFIED
Date : 26 September 2014
Identification Number : 10.1109/TCYB.2014.2360074
Uncontrolled Keywords : Evolutionary algorithms, image identification, invariant feature extraction, multiobjective optimization
Related URLs :
Additional Information : (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 25 Sep 2015 16:18
Last Modified : 25 Sep 2015 16:18
URI: http://epubs.surrey.ac.uk/id/eprint/808554

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