PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Modelling image complexity by independent component analysis, with application to content-based image retrieval
Jukka Perkiö and Aapo Hyvärinen
In: 19th International Conference on Artificial Neural Networks, 14-17 Sep 2009, Limassol, Cyprus.

Abstract

Estimating the degree of similarity between images is a challenging task as the similarity always depends on the context. Because of this context dependency, it seems quite impossible to create a universal metric for the task. The number of low-level features on which the judgement of similarity is based may be rather low, however. One approach to quantifying the similarity of images is to estimate the (joint) complexity of images based on these features. We present a novel method to estimate the complexity of images, based on ICA. We further use this to model joint complexity of images, which gives distances that can be used in content-based retrieval. We compare this new method to two other methods, namely estimating mutual information of images using marginal Kullback-Leibler divergence and approximating the Kolmogorov complexity of images using Normalized Compression Distance.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:5967
Deposited By:Jukka Perkiö
Deposited On:08 March 2010