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Texture image classification using pixel N-grams

conference contribution
posted on 2017-03-27, 00:00 authored by P Kulkami, A Stranieri, Julien UgonJulien Ugon
© 2016 IEEE. Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial relationships between the visual words. Here, a novel image representation 'Pixel N-grams', inspired from the character N-gram concept in text retrieval has been applied for texture classification purpose. Texture is an important property for image classification. Experiments on the benchmark texture database (UIUC) demonstrates that the overall classification accuracy resulting from Pixel N-gram approach (89.5%) is comparable with that achieved using BoVW approach (84.4%) with the added advantage of simplicity and reduced computational cost.

History

Event

Signal and Image Processing. Conference (2016 : Beijing, China)

Pagination

137 - 141

Publisher

IEEE

Location

Beijing, China

Place of publication

Piscataway, N.J.

Start date

2016-08-13

End date

2016-08-15

ISBN-13

9781509023769

Language

eng

Publication classification

X Not reportable; EN.1 Other conference paper

Copyright notice

2016, IEEE

Title of proceedings

ICSIP 2016 : Proceedings of the IEEE International Conference on Signal and Image Processing

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