An evolutionary patch pattern approach for texture discrimination

A new evolutionary approach is presented, based on implicit pattern–process relationships. For implementing this approach, any gray level texture image is decomposed into a progressive sequence of binary patch patterns that describe a process of change from background to foreground domination. Each of the binary patterns throughout these sequences is parameterized, using several metrics that describe, for example, its fragmentation level, both for the background (e.g., white) and foreground (e.g., black) patch patterns. Any texture type is then assumed to have a unique evolutionary path represented by a distinctive region in the feature space of metrics characterizing these patterns and their change. Application of hierarchical clustering based on a few (3 or 4) metrics representing characteristic stages in the patterns’ change process allowed us to accurately discriminate between 50 samples of 10 Brodatz texture types.