A Compression Based Distance Measure for Texture

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 overfitting. In this work we propose to extended 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. We show that by approximating the Kolmogorov complexity with state-of-the-art image compressors such as MPEG, we can create an efficient and robust parameter-free texture similarity measure. We demonstrate the utility of our ideas 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.