Coherency Preserving Feature Transformation for Semantic Segmentation

Kenji Watanabe

Abstract: Semantic segmentation is an object classification such as a perceptual grouping in image analysis tasks. And semantic segmentation methods have been proposed to improve the pixel-wise classification accuracies in the research field of computer vision. Recently, the methods achieve high accuracies by using convolutional neural networks (CNN). These CNN-based methods generally need large number and variety of training images to assure a generalization performance without fine tuning. However, it is difficult to prepare the large-scale image datasets which were given reliable semantic labels. In order to overcome this problem, we propose an efficient method for feature transformation to improve the generalization performance in semantic segmentation. Our method is formulated as a variant of neighborhood preserving embedding (NPE) incorporating the within-class coherency and showed best generalization performances compared with other feature transformation methods.