Title
General Framework for Rotation Invariant Texture Classification Through Co-occurrence of Patterns
Abstract
The use of co-occurrences of patterns in image analysis has been recently suggested as one of the possible strategies to improve on the bag-of-features model. The intrinsically high number of features of the method, however, is a potential limit to its widespread application. Its extension into rotation invariant versions also requires careful consideration. In this paper we present a general, rotation invariant framework for co-occurrences of patterns and investigate possible solutions to the dimensionality problem. Using local binary patterns as bag-of-features model, we experimentally evaluate the potential advantages that co-occurrences can provide in comparison with bag-of-features. The results show that co-occurrences remarkably improve classification accuracy in some datasets, but in others the gain is negligible, or even negative. We found that this surprising outcome has an interesting explanation in terms of the degree of association between pairs of patterns in an image, and, in particular, that the higher the degree of association, the lower the gain provided by co-occurrences in comparison with bag-of-features.
Year
DOI
Venue
2014
10.1007/s10851-014-0500-9
Journal of Mathematical Imaging and Vision
Keywords
DocType
Volume
Support Vector Machine,Local Binary Pattern,Feature Selection Scheme,Uniform Local Binary Pattern,Oriented Pair
Journal
50
Issue
ISSN
Citations 
3
0924-9907
5
PageRank 
References 
Authors
0.41
25
3
Name
Order
Citations
PageRank
Elena González1151.99
Antonio Fernández 0003250.41
Francesco Bianconi350.74