Plant identification from bark: A texture description based on Statistical Macro Binary Pattern

This paper presents a novel, yet compact texture descriptor for plant species identification based on bark texture images. Termed Statistical Macro Binary Pattern (SMBP), the descriptor is informative, rotation invariant, and it is designed to encode texture information from a large support area. The main novelty of this approach is the use of statistical description to represent the intensity distribution in the large support area, and an LBP-like encoding scheme to derive a statistical macro pattern by thresholding it against its adaptive statistical prototype. We propose to test three neighborhood sampling schemes according to the angular quantization at each level of the macrostructure. The comprehensive experiments on three challenging bark datasets (BarkTex, Trunk12, AFF) show that our descriptor achieves high and more consistent identification rates when compared with LBP-like texture descriptors.