- Title
- Comparison of pixel N-Grams with histogram, Haralick's features and bag-of-visual-words for texture image classification
- Creator
- Kulkarni, Pradnya; Stranieri, Andrew
- Date
- 2018
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180880
- Identifier
- vital:15820
- Identifier
-
https://doi.org/10.1109/I2CT.2018.8529815
- Identifier
- ISBN:978-1-5386-4273
- Abstract
- Texture image classification is very useful in many domains. It has been tried using statistical, spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram, Haralick’s features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore, Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas, they were comparable with the BoVW approach.
- Publisher
- IEEE
- Relation
- IEEE 3rd International Conference on Convergence in Technology: Pune, India ; April 6th-8th, 2018 p. 1-4
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Subject
- Pixel N-grams; Image classfication; Texture; Histogram; Co-occurence matrix; Bag-of-Visual-Words
- Reviewed
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