Feature fusion and non-negative matrix factorization based active contours for texture segmentation

This paper presents a robust and convex active contour model for texture segmentation. Firstly, to achieve more comprehensive feature description, we compute a set of feature maps by combining local variation degree (LVD) of intensity and Gabor features. This feature fusion improves the separability between sub-regions and the robustness against complex textures. Upon these feature maps, we compute local histograms over fixed-size windows to describe the local structures formed by feature values. For each pixel, its feature vector is defined as the concatenation of all computed histograms. Secondly, to localize region boundaries more accurately, we formulate the proposed energy functional via Non-negative Matrix Factorization (NMF), which encourages each pixel to fall into the sub-region that has the largest coverage area in its neighborhood. Finally, the functional is explored further using convex optimization theory. Our segmentation results are therefore insensitive to different initial contours. The experiments performed on synthetic images, histology images and natural images demonstrate that our approach can obtain high-quality object boundaries in the presence of image noise and cluttered scenes. (C) 2019 Elsevier B.V. All rights reserved.