PPML: Metric Learning with Prior Probability for Video Object Segmentation Video object segmentation plays an important role in computer vision and has attracted much attention. Although many recent works have removed the finetuning process in pursuit of fast inference speed, while achieving high segmentation accuracy, they are still far from being realtime. In this paper, we regard this task as a feature matching problem and propose a prior probability based metric learning (PPML) method for faster inference speed and higher segmentation accuracy. The proposed method consists of two ingredients: a novel template space updating strategy that improves the efficiency of segmentation by avoiding the explosion of data in template space, and a novel feature matching method which applies more potential probability information through integrating the prior of the first frame and the predicted score of previous frames. Experimental results on DAVIS datasets demonstrate that the proposed method reaches the stateoftheart competitive performance and is more efficient in time consumption.

Edge Prior Multilayer Segmentation Network Based on Bayesian Framework In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set.