We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions. The resulting layers from decomposition pave the way for applications that benefit from layer-based editing, such as recol-oring and compositing of images and videos. The current state-of-the-art approach for this problem is hindered by slow processing time due to its iterative nature, and consequently does not scale to certain real-world scenarios. To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass. Furthermore, our method separately decomposes the color layers and the al- pha channel layers. By leveraging a novel training objective, our method achieves proper assignment of colon amongst layers. As a consequence, our method achieve promising quality without existing issue of inference speec for iterative approaches. Our thorough experimental analysis shows that our method produces qualitative and quantitative results comparable to previous methods while achieving a 300,000x speed improvement. Finally, we utilize ow proposed method on several applications, and demonstrate its speed advantage, especially in video editing.
|Publication status||Published - 2020 Apr 17|
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