Elements of information theory
Elements of information theory
Complexity
Digital Image Processing
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Computer Vision
SBIA '98 Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Hierarchical Classifier Design Using Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning pattern classification-a survey
IEEE Transactions on Information Theory
On the convexity of some divergence measures based on entropy functions
IEEE Transactions on Information Theory
An information-theoretic framework for image complexity
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Object-adaptive tracking for AR guidance system
VRCAI '08 Proceedings of The 7th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
A fuzzy approach to the evaluation of image complexity
Fuzzy Sets and Systems
Estimating watermarking capacity in gray scale images based on image complexity
EURASIP Journal on Advances in Signal Processing
Structural similarity image quality reliability
Signal Processing
Global contrast factor - a new approach to image contrast
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
An information-theoretic framework for image complexity
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Special Section on CAD/Graphics 2013: Confidence-driven image co-matting
Computers and Graphics
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In 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.