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Evolutionary Data Purification for Social Media Classification

James, S and Collomosse, John (2017) Evolutionary Data Purification for Social Media Classification In: 23rd International Conference on Pattern Recognition (ICPR 2016), 2016-12-04 - 2016-12-08, Cancun, Mexico.

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We present a novel algorithm for the semantic labeling of photographs shared via social media. Such imagery is diverse, exhibiting high intra-class variation that demands large training data volumes to learn representative classifiers. Unfortunately image annotation at scale is noisy resulting in errors in the training corpus that confound classifier accuracy. We show how evolutionary algorithms may be applied to select a ’purified’ subset of the training corpus to optimize classifier performance. We demonstrate our approach over a variety of image descriptors (including deeply learned features) and support vector machines.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
James, S
Date : 24 April 2017
Funders : EPSRC
DOI : 10.1109/ICPR.2016.7900039
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 07 Sep 2016 08:34
Last Modified : 11 Dec 2018 11:22

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