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Comparison of Pixel N-Grams with Histogram, Haralick's features and Bag-of-Visual-Words for Texture Image Classification
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Pages: 1 - 5
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 BoVwapproach.
About the journal
Journal2018 3rd International Conference for Convergence in Technology, I2CT 2018