Dynamic Texture Analysis Using Auto-correlation Function of Histogram Similarity Measure From Galois-Field Texture Representation of Water Flow Video

Abstract

This paper propose a method for determining the periodicity of dynamic textures from water video using spatial feature similarity measure of Galois-Field (GF) texture representation. Auto correlation function are use to analyze the extracted spatial feature from the representation to determine the periodicity of dynamic textures. There are two type of spatial feature to be compared, the first is histogram and second is normalize cumulative histogram (NCH). Two type of experiment are conducted, the first is virtual rotation where video is rotated virtually from 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> until 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> , the second is actual rotation where camera are rotated physically. Experiments show that although GF improves the performance of the Histogram similarity measure, overall NCH shows better performance. In virtual rotation experiment, GF representation prove to minimize variability due to rotation of camera, the maximum variability produce by NCH is 27%, while when GF are not use the maximum variability is 106%. Contrary, in actual rotation experiment, GF is not proven to minimize variability where NCH produce maximum variability is 57%, while where GF are not use the maximum variability is 9%. The difference in variability pattern between virtual and actual rotation, shows that Galois Field is good at handling dynamic texture rotation, but not against other factors that affect variability.