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Evolutionary Data Purification for Social Media Classification

James, S and Collomosse, JP (2016) 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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Abstract

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 :
AuthorsEmailORCID
James, SUNSPECIFIEDUNSPECIFIED
Collomosse, JPUNSPECIFIEDUNSPECIFIED
Date : 2016
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 07 Sep 2016 08:34
Last Modified : 07 Sep 2016 08:34
URI: http://epubs.surrey.ac.uk/id/eprint/812000

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