- Title
- Texture image classification using pixel N-grams
- Creator
- Kulkarni, Pradnya; Stranieri, Andrew; Ugon, Julien
- Date
- 2016
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180775
- Identifier
- vital:15822
- Identifier
-
https://doi.org/10.1109/SIPROCESS.2016.7888239
- Identifier
- ISBN:978-1-5090-2377-6
- Abstract
- 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.
- Publisher
- IEEE
- Relation
- 2016 IEEE International Conference on Signal and Image Processing (ICSIP); Beijing, China; 13-15 Aug, 2016 p. 137-141
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Beijing, China
- Rights
- Copyright IEEE
- Subject
- Visualization; Image classification; Gabor filters; Vocabulary; Transforms; Computational modeling; Databases
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