Texture image classification using pixel N-grams

TLDR: Experiments on the benchmark texture database demonstrates that the overall classification accuracy resulting from Pixel N-gram approach is comparable with that achieved using BoVW approach with the added advantage of simplicity and reduced computational cost.

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.