DEL: Deep Embedding Learning for Efficient Image Segmentation

Image segmentation has been explored for many years and still remains a crucial vision problem. Some efficient or accurate segmentation algorithms have been widely used in many vision applications. However, it is difficult to design a both efficient and accurate image segmenter. In this paper, we propose a novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation. Starting with the SLIC superpixels, we train a fully convolutional network to learn the feature embedding space for each superpixel. The learned feature embedding corresponds to a similarity measure that measures the similarity between two adjacent superpixels. With the deep similarities, we can directly merge the superpixels into large segments. The evaluation results on BSDS500 and PASCAL Context demonstrate that our approach achieves a good trade-off between efficiency and effectiveness. Specifically, our DEL algorithm can achieve comparable segments when compared with MCG but is much faster than it, i. e. 11. 4fps vs. 0. 07fps.

Authors

• Yun Liu College of Artificial Intelligence, Southwest University, Chongqing, China College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore
• Peng-tao Jiang vivo Mobile Communication Co., Ltd
• Vahan Petrosyan
• Shi-Jie Li
• Jiawang Bian
• Le Zhang Fuxi AI Lab, Netease
• Ming-Ming Cheng Nankai University NKIARI

Keywords

• Computer Vision: 2D and 3D Computer Vision
• Computer Vision: Computer Vision