DEL: Deep Embedding Learning for Efficient Image Segmentation

Yun Liu, Peng-Tao Jiang, Vahan Petrosyan, Shi-Jie Li, Jiawang Bian, Le Zhang, Ming-Ming Cheng

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Main track. Pages 864-870.

Abstract

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.