Active Matting
Image matting is an ill-posed problem. It requires a user input trimap or some strokes to obtain an alpha matte of the foreground object. A fine user input is essential to obtain a good result, which is either time consuming or suitable for experienced users who know where to place the strokes. In this paper, we explore the intrinsic relationship between the user input and the matting algorithm to address the problem of where and when the user should provide the input. Our aim is to discover the most informative sequence of regions for user input in order to produce a good alpha matte with minimum labeling efforts. To this end, we propose an active matting method with recurrent reinforcement learning. The proposed framework involves human in the loop by sequentially detecting informative regions for trivial human judgement. Comparing to traditional matting algorithms, the proposed framework requires much less efforts, and can produce satisfactory results with just 10 regions. Through extensive experiments, we show that the proposed model reduces user efforts significantly and achieves comparable performance to dense trimaps in a user-friendly manner. We further show that the learned informative knowledge can be generalized across different matting algorithms.
Due to the importance of the user input trimap or strokes in image matting, this paper propose an active matting method with recurrent reinforcement learning. The proposed model can actively suggest a small number of informative sequence of regions for user to label, which minimizes the labeling efforts. Experimental results show that the proposed model achieves comparable performance to dense trimaps. In addition, the learned informative knowledge can be applied to a variety of matting algorithms as shown in this paper.  Strengths: 1. This paper proposes a novel an helpful concept named informative regions to denote the regions that affect the matting performance most.  2. The proposed model is in a user-friendly manner that users are only required to label a suggested region as foreground or background. 3. Extensive results show that the learned informative regions achieve comparable matting performance to a fine-labelled trimap in terms of quality while the proposed method is much faster. 4. The proposed model have well applicability to a variety of matting algorithms. 5. The details are well clarified and the method can be reproduced according to this paper. Weaknesses: 1. The authors run their network S times repeatedly and collect S latent regions as suggestions each iteration. They set S to 2 to achieve a good balance between accuracy and efficiency. More details are missing to support this choice. 2. Several important references are missing. For example, 1) Ning Xu, Brian Price, Scott Cohen, Thomas Huang, Deep Image Matting. CVPR 2017. the authors are encouraged to add comparisons.  2) Liang Yang, Xiaochun Cao, Di Jin, Xiao Wang, Dan Meng: A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization. IEEE Trans. Cybernetics 45(11): 2585-2598 (2015) where a similar "active" must-link priors are utilized.  3) Chuan Wang, Hua Zhang, Liang Yang, Xiaochun Cao, Hongkai Xiong: Multiple Semantic Matching on Augmented N-Partite Graph for Object Co-Segmentation. IEEE Trans. Image Processing 26(12): 5825-5839 (2017) where co-matting is addressed. 3. In my point of view, the most important significance of this paper is that is can help establishing large-scale matting dataset, which is rarely mentioned in this paper.