Divide and Conquer: A Self-Adaptive Approach for High-Resolution Image Matting

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 b...

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Bibliographic Details
Published in2016 International Conference on Virtual Reality and Visualization (ICVRV) pp. 24 - 30
Main Authors Guangying Cao, Jianwei Li, Xiaowu Chen, Zhiqiang He
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Benchmark testing
high-resolution
Image color analysis
image matting
Image resolution
Laplace equations
Memory management
Merging
Optimization
patch division
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Summary: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.
DOI:10.1109/ICVRV.2016.13