Automatic trimap generation and artifact reduction in alpha matte using unknown region detection

Abstract

Image Matting is an actively researched topic due to its massive impact on the filmmaking industry. It refers to the accurate extraction of the foreground from an image and is a severely under-constrained problem. To constrain this issue, user input in the form of a trimap is required. However, users must generate this trimap manually, which is an exhausting and time-consuming process. To tackle this issue, we develop an expert system which is capable of generating trimaps automatically without any user interaction. We utilize three different techniques which use machine learning at the core. Our expert system receives knowledge from these three techniques which are processed by our system to generate trimaps automatically. We strongly believe that our study will have a strong impact on the filmmaking industry. Our study lays the groundwork for automatic trimap generation. Furthermore, our study can be applied to expert and intelligent systems related to image retrieval and automatic foreground/primary object segmentation in images or videos. We propose a simple yet effective approach to generate optimal trimaps automatically by combining image saliency, graph cut segmentation (lazy snapping), and fuzzy c-means clustering (FCM). Lazy snapping is an interactive segmentation technique that requires foreground and background scribbles as input. Instead of using user-provided foreground scribbles, we utilize a saliency map as foreground scribbles and input it to the lazy snapping. This results in a coarsely segmented foreground object. We use the corners as the background scribbles based on the assumption that most foreground objects are located in the center of the image. To generate an optimal trimap, we locally cluster the boundary region of the foreground segmentation using FCM. We tested our algorithm on alpha matting evaluation and salient object datasets. In addition to generating accurate trimaps automatically, the alpha mattes generated by our optimal trimaps contain fewer artifacts as compared to trimaps generated by previous works. Moreover, the alpha mattes computed using our optimal trimaps were computed faster as compared to computing alpha mattes with trimaps from previous works. We showed in our experiments that optimal trimaps can improve alpha matte quality by reducing artifacts. Finally, our approach does not rely on depth data like the previous methods. Our experiments show the effectiveness of our method.