Image classification via LZ78 based string kernel: a comparative study
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Content-based facial image retrieval using constrained independent component analysis
Information Sciences: an International Journal
Image similarity: from syntax to weak semantics
Multimedia Tools and Applications
Classifying stem cell differentiation images by information distance
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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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.