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ICANN
2009
Springer

Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval

9 years 25 days ago
Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval
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. Key words: Image complexity; ICA; NCD; Kolmogorov complexity
Jukka Perkiö, Aapo Hyvärinen
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICANN
Authors Jukka Perkiö, Aapo Hyvärinen
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