Authors:
David Gustavsson
;
Kim Steenstrup Pedersen
and
Mads Nielsen
Affiliation:
University of Copenhagen, Denmark
Keyword(s):
Image complexity measure, Geometry, Texture, Singular value decomposition, SVD, Truncated singular value decomposition, TSVD, Matrix norm.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Quality
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
Abstract:
Images are composed of geometric structures and texture, and different image processing tools - such as denoising, segmentation and registration - are suitable for different types of image contents. Characterization of the image content in terms of geometric structure and texture is an important problem that one is often faced with. We propose a patch based complexity measure, based on how well the patch can be approximated using singular value decomposition. As such the image complexity is determined by the complexity of the patches. The concept is demonstrated on sequences from the newly collected DIKU Multi-Scale image database.