PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

evaluating dimensionality reduction techniques for visual categorization using renyi entropy
Ashish Gupta and Richard Bowden
In: EUSIPCO 2011, 29 Aug - 02 Sep 2011, Barcelona, Spain.

Abstract

Visual category recognition is a difficult task of significant interest to the machine learning and vision community. One of the principal hurdles is the high dimensional feature space. This paper evaluates several linear and non-linear dimensionality reduction techniques. A novel evaluation metric, the renyi entropy of the inter-vector euclidean distance distribution, is introduced. This information theoretic measure judges the techniques on their preservation of structure in lower-dimensional sub-space. The popular dataset, Caltech-101 is utilized in the experiments. The results indicate that the techniques which preserve local neighbourhood structure performed best amongst the techniques evaluated in this paper.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8514
Deposited By:Ashish Gupta
Deposited On:06 February 2012