Classification of texture based on Bag-of-Visual-Words through complex networks

• Published in: Expert systems with applications Vol. 133; pp. 215 - 224
• Main Authors: de Lima, Geovana V.L., Saito, Priscila T.M., Lopes, Fabricio M., Bugatti, Pedro H.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.11.2019; Elsevier BV
• Subjects: Bag-of-visual words; Classification; Complex networks; Computing costs; Cost analysis; Image detection; Networks; Texture; Texture; Classification; Bag-of-visual words; Complex networks
• DOI: 10.1016/j.eswa.2019.05.021
• ISSN: 0957-4174

Highlights•A novel texture descriptor based on Bag-of-Visual-Words through Complex Networks.•The proposed methodology relies on relevant measures from the complex networks.•It provides better image description using different threshold and vocabulary sizes.•It achieved better results when compared with state-of-the-art methods.•It was evaluated using different classifiers and datasets. Over the last years complex data (e.g. images) have been growing in a very fast pace. This demands the ability to describe and to categorize them. To solve this problem it is essential to develop efficient and effective vision-based expert techniques. Hence, the cornerstone of our work is to propose a new methodology, called BoVW-CN, that combines Bag-of-Visual-Words and complex networks for describing keypoints detected in a given image. Our insight is that describing just the relevant points of an image we can achieve a more cost-effective and better image description. The obtained results testify that BoVW-CN, applied to public image datasets, outperforms the widely used state-of-the-art methods. We not only obtained good accuracies (e.g. 78.18%), but also performed analyses to find the best trade-off between computational cost and accuracy. Besides, to the best of our knowledge, our work is the first one to propose such integration of Bag-of-Visual-Words and complex networks through a texture-based focus.