An Information-Theoretic Framework for Image Complexity

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dc.contributor.authorRigau, Jaumeen_US
dc.contributor.authorFeixas, Miquelen_US
dc.contributor.authorSbert, Mateuen_US
dc.contributor.editorLaszlo Neumann and Mateu Sbert and Bruce Gooch and Werner Purgathoferen_US
dc.date.accessioned2013-10-22T07:40:24Z
dc.date.available2013-10-22T07:40:24Z
dc.date.issued2005en_US
dc.description.abstractIn this paper, we introduce a new information-theoretic approach to study the complexity of an image. The new framework we present here is based on considering the information channel that goes from the histogram to the regions of the partitioned image, maximizing the mutual information. Image complexity has been related to the entropy of the image intensity histogram. This disregards the spatial distribution of pixels, as well as the fact that a complexity measure must take into account at what level one wants to describe an object. We define the complexity by using two measures which take into account the level at which the image is considered. One is the number of partitioning regions needed to extract a given ratio of information from the image. The other is the compositional complexity given by the Jensen-Shannon divergence of the partitioned image.en_US
dc.description.seriesinformationComputational Aesthetics in Graphics, Visualization and Imagingen_US
dc.identifier.isbn3-905673-27-4en_US
dc.identifier.issn1816-0859en_US
dc.identifier.urihttps://doi.org/10.2312/COMPAESTH/COMPAESTH05/177-184en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.3 [Computing Methodologies]: Computer GraphicsPicture/ Image Generation; I.4.0 [Computing Methodologies]: Image Processing and Computer VisionImage Processing Software; I.4.6 [Computing Methodologies]: Image Processing and Computer Vision Segmentationen_US
dc.titleAn Information-Theoretic Framework for Image Complexityen_US
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