PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fuzzy Encoding For Image Classification Using Gustafson-Kessel Aglorithm
Ashish Gupta and Richard Bowden
In: ICIP2012, Florida, USA(2012).

Abstract

This paper presents a novel adaptation of fuzzy clustering and feature encoding for image classification. Visual word ambiguity has recently been successfully modelled 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.

EPrint Type:Conference or Workshop Item (Other)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:9143
Deposited By:Ashish Gupta
Deposited On:21 February 2012