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Microscale-Searching Evolutionary Optimization for Image Matting
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AI
DOI：10.1109/TETCI.2025.3637791.png)
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Abstract
En 中文
Image matting is a fundamental computer vision task. It is a large-scale optimization problem involving the selection of pixel pairs from pixel-pair sets of an image. Less prior, such as the trimaps of images, is required for evolutionary algorithms (EAs) to solve the image matting problem compared to deep learning-based methods. However, it is challenging for EAs to solve the image matting problem efficiently due to the large size of the decision set. This paper proposes a framework to guide EAs to search in a microscale subset of the decision set. The subset is estimated by collecting best-so-far solutions during EAs solve similar subproblems. Experimental results demonstrated that by integrating the proposed framework, EAs require fewer FEs to achieve competitive results compared to their original versions. Additionally, the results also indicate that the proposed strategy enhance the performance of EAs in terms of mean squared error and connectivity metrics compared to other EAs-based methods in most cases. The contribution of our work is to make EAs efficient algorithms to solving the image matting problem in scenarios with weak prior.
Keywords:
Image matting
microscale-searching
large-scale optimization
Journal
I
IF:
6.5
Papers:
1.4K
Citations:
4.5K
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