A compression-based distance measure for texture
Statistical analysis and data mining
The analysis of texture is an important subroutine in application areas as diverse as biology, medicine, robotics, and forensic science. While the last three decades have seen extensive research in algorithms to measure texture similarity, almost all existing methods require the careful setting of many parameters. There are many problems associated with a surfeit of parameters, the most obvious of which is that with many parameters to fit, it is exceptionally difficult to avoid over fitting. In
... this work we propose to extend recent advances in Kolmogorov complexity-based similarity measures to texture matching problems. These Kolmogorov based methods have been shown to be very useful in intrinsically discrete domains such as DNA, protein sequences, MIDI music and natural languages; however, they are not well defined for realvalued data. Towards this, we introduce the Campana-Keogh (CK) video compression based method for texture measures. These measures utilize state-of-theart video compressors to approximate the Kolmogorov complexity. Using the CK method, we create an efficient and robust parameter-free texture similarity measure, the CK-1 distance measure. We demonstrate the utility of our measure with an extensive empirical evaluation on real-world case studies drawn from nematology, arachnology, entomology, medicine, forensics, ecology, and several well known texture analysis benchmarks.