Random projections and Single BoW for fast and Robust texture segmentation

Abstract

This paper develops a theoretically simple, efficient yet robust approach to supervised texture segmentation based on local radial difference features and a Bag-of-Words (BoW) model. We make the following contributions: (1) we propose an approach to optimally learn new compact single BoW histogram models from the entire training set, with the single histogram providing benefits of efficiency in both memory and computation costs; (2) we show that BoW histograms computed from local simple radial difference features can provide an accurate pixel-wise segmentation of a textured image; and (3) we investigate whether sparse reconstruction, very successful in texture classification, assists in texture segmentation, with our study demonstrating the surprising conclusion that sparse reconstruction methods actually do not improve segmentation performance.Extensive experiments on composite natural texture images demonstrate the superiority of the proposed approach over multiple state of the art texture segmentation methods. Our experimental evaluation demonstrates a significant superiority over recent popular sparse reconstruction segmentation methods in terms of computational efficiency, while outperforming or comparable in terms of segmentation accuracy.