Divide and Conquer: A Self-Adaptive Approach for High-Resolution Image Matting
Guangying Cao, Jianwei Li, Zhiqiang He, Xiaowu Chen · 2016
This paper proposes an efficient patch-based image matting approach to reduce memory consumption in processing high-resolution images. Most existing image matting techniques employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory consumption. Inspired by 'divide and conquer', the image is divided into small patches self-adaptively according to the distribution of pixels, then the matting algorithm is applied in patch level. The memory consumption is related to the size of patch. Relationships between patches are also considered by locally linear embedding to maintain consistency through the whole image. Experimental results show that our method significantly reduces memory consumption while maintaining high-fidelity matting results on the benchmark dataset.