Texture image segmentation using fused features and active contour

• Published in: 2016 23rd International Conference on Pattern Recognition (ICPR) pp. 2036 - 2041
• Main Authors: Mingqi Gao, Hengxin Chen, Shenhai Zheng, Bin Fang, Lin Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Active contours; Computational modeling; Feature extraction; Histograms; Image segmentation; Lighting; Robustness
• DOI: 10.1109/ICPR.2016.7899935

This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion and isolated region detection operations to eliminate image patches unrelated with object regions. And then the fused features combined LSVD with Gabor can be constructed to express image structure in many scene. During the texture segmentation stage, a factorization based fitting energy is proposed to measure the weights of representative features in the features computed from image regions. This fitting energy can be used to localize region boundary more accurately. Moreover, a boundary shrinking method is put forward to improve the reliability of representative features. By comparing our proposed method with the recent texture segmentation models on synthetic images and natural images, we demonstrate that the novel active contour model can obtain accurate segmentation results and is robust to noise, illumination and position of initial contour.