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Fuzzy encoding for image classification using Gustafson-Kessel algorithm

Gupta, A and Bowden, R (2012) Fuzzy encoding for image classification using Gustafson-Kessel algorithm Proceedings - International Conference on Image Processing, ICIP. pp. 3137-3140.

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Abstract

This paper presents a novel adaptation of fuzzy clustering and feature encoding for image classification. Visual word ambiguity has recently been successfully modeled by kernel codebooks to provide improvement in classification performance over the standard 'Bag-of-Features' (BoF) approach, which uses hard partitioning and crisp logic for assignment of features to visual words. Motivated by this progress we utilize fuzzy logic to model the ambiguity and combine it with clustering to discover fuzzy visual words. The feature descriptors of an image are encoded using the learned fuzzy membership function associated with each word. The codebook built using this fuzzy encoding technique is demonstrated to provide superior performance over BoF. We use the Gustafson-Kessel algorithm which is an improvement over Fuzzy C-Means clustering and can adapt to local distributions. We evaluate our approach on several popular datasets and demonstrate that it consistently provides superior performance to the BoF approach. © 2012 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Gupta, AUNSPECIFIEDUNSPECIFIED
Bowden, RUNSPECIFIEDUNSPECIFIED
Date : 1 December 2012
Identification Number : 10.1109/ICIP.2012.6467565
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 10:56
Last Modified : 31 Oct 2017 17:46
URI: http://epubs.surrey.ac.uk/id/eprint/808974

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