Open AccessMeasuring meaningful information in images: algorithmic specified complexity
Author(s) -
Ewert Winston,
Dembski William A.,
Marks Robert J.
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0141
Subject(s) - kolmogorov complexity , computer science , artificial intelligence , image (mathematics) , meaning (existential) , context (archaeology) , computational complexity theory , noise (video) , measure (data warehouse) , simple (philosophy) , pattern recognition (psychology) , computer vision , theoretical computer science , algorithm , data mining , psychology , paleontology , philosophy , epistemology , psychotherapist , biology
Both Shannon and Kolmogorov–Chaitin–Solomonoff (KCS) information models fail to measure meaningful information in images. Pictures of a cow and correlated noise can both have the same Shannon and KCS information, but only the image of the cow has meaning. The application of ‘algorithmic specified complexity’ (ASC) to the problem of distinguishing random images, simple images and content‐filled images is explored. ASC is a model for measuring meaning using conditional KCS complexity. The ASC of various images given a context of a library of related images is calculated. The ‘portable network graphic’ (PNG) file format's compression is used to account for typical redundancies found in images. Images which containing content can thereby be distinguished from those containing simply redundancies, meaningless or random noise.