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IEEE Trans Neural Netw. 2010 Dec;21(12):1990-6. doi: 10.1109/TNN.2010.2090047. Epub 2010 Nov 11.

Linear discriminant analysis for signatures.

Author information

1
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA. seungilh@cs.cmu.edu

Abstract

We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to LDA, which is one of the main limitations of classical LDA. Therefore, signature-LDA minimizes the loss of intrinsic information of local image features while selecting more discriminating features using label information. Empirical evidence on texture databases shows that signature-LDA improves upon state-of-the-art approaches for texture image classification and outperforms other feature selection methods for local image features.

PMID:
21075720
DOI:
10.1109/TNN.2010.2090047
[Indexed for MEDLINE]

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