About the authors
* is a Lecturer in the Department of Industrial
Engineering at the University of Perugia, Italy.
Senior Lecturer in the School of Industrial Engineering at the
University of Vigo, Spain.
is a Senior Lecturer in the School of Industrial Engineering
at the University of Vigo, Spain.
This paper investigates the problem of learning
sets of discriminative patterns from Local Binary Patterns
(LBP). Such patterns are usually referred to as ‘Dominant
Local Binary Patterns’ (DLBP). The strategies to obtain the
dominant patterns may either keep knowledge of the patterns
labels or discard it. It is the aim of this work to
determine which is the best option. To this end the paper
studies the effectiveness of different strategies in terms
of accuracy, data compression ratio and time complexity. The
results show that DLBP provides a significant compression
rate with only a slight accuracy decrease with respect to
LBP, and that retaining information about the patterns’
labels improves the discrimination capability of DLBP.
Theoretical analysis of time complexity revealed that the
gain/loss provided by DLBP vs. LBP depends on the
classification strategy: we show that, asymptotically, there
is in principle no advantage when classification is based on
computationally-cheap methods (such as nearest neighbour and
nearest mean classifiers), because in this case determining
the dominant patterns is computationally more expensive than
classifying using the whole feature vector; by contrast,
pattern selection can be beneficial with more complex
classifiers such as support vector machines.
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