Authors:
Timo Ahonen
and
Matti Pietikäinen
Affiliation:
Machine Vision Group, University of Oulu, Finland
Keyword(s):
Local Binary Pattern, KTH-TIPS, MR8, Gabor, Texture descriptor.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper presents a new unified framework for texture descriptors such as Local Binary Patterns (LBP) and Maximum Response 8 (MR8) that are based on histograms of local pixel neighborhood properties. This framework is enabled by a novel filter based approach to the LBP operator which shows that it can be seen as a special filter based texture operator. Using the proposed framework, the filters to implement LBP are shown to be both simpler and more descriptive than MR8 or Gabor filters in the texture categorization task. It is also shown that when the filter responses are quantized for histogram computation, codebook based vector quantization yields slightly better results than threshold based binning at the cost of higher computational complexity.