Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Semantic Organization of Scenes Using Discriminant Structural Templates
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Second order image statistics in computer graphics
APGV '04 Proceedings of the 1st Symposium on Applied perception in graphics and visualization
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Image statistics for clustering paintings according to their visual appearance
Computational Aesthetics'09 Proceedings of the Fifth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
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Amplitude spectra of natural images look surprisingly alike. Their shape is governed by the famous 1/f power law. In this work we propose a novel low parameter model for describing these spectra. The Sum-of-Superellipses conserves their common falloff behavior while simultaneously capturing the dimensions of variation--concavity, isotropy, slope, main orientation--in a small set of meaningful illustrative parameters. We demonstrate its general usefulness in standard computer vision tasks like scene recognition and image compression.