Unsupervised segmentation of multiview feature semantics by hashing model

Abstract

Abstract The image segmentation of finely detailed object in images is becoming increasingly important in various image applications. Herein, multiview features such as color, spatial features, texture, saliency, and depth are used to calculate the feature semantic that differentiates the image foreground and background at the pixel level. For different image type, the importance of every single image feature is various. We assume that combining multiple features will produce improved results compared to using any single feature and propose an unsupervised learning strategy to learn optimal feature projection to the final solution surface of binary segmentation. To reduce computational complexity, a hashing model is used to represent the projected feature using binary codes, as its hamming distance efficiently retrieves the pixel similarity. Comparative experiments using the current state-of-the-art unsupervised segmentation algorithms and a deep learning approach, i.e., a fully convolutional network, demonstrate the advantages of the proposed method.