Research on foreground subject segmentation algorithm with high similarity of foreground background

Yating Shen; Lujie Zeng; Ping Zong

doi:10.1117/12.3009537

16 October 2023 Research on foreground subject segmentation algorithm with high similarity of foreground background

Yating Shen, Lujie Zeng, Ping Zong

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030W (2023) https://doi.org/10.1117/12.3009537
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

One of the advanced algorithms for image foreground segmentation is DIM (Deep Image Matting), which applies convolutional neural networks. However, for complex foreground shapes, fixed convolutional kernels are not the optimal approach. Therefore, Deformable Convolution Networks (DCN) were introduced as a replacement, which partially address the issue of losing edge details in deep layers. However, DCN suffers from the extraction of undesired features, significantly reducing the model’s flexibility. To address this, the bias is reshaped using a balanced refining bias with an adaptive weight matrix, instead of a fixed 9-grid bias. To evaluate the effectiveness of the bias, a simple model called DIM_RDC is designed and trained, which utilizes the balanced refining bias. The model is tested on the Adobe dataset, and the results show that DIM_RDC improves the evaluation metrics of SAD by 1.62% and MSE by 11.05% compared to DIM. Therefore, DIM_RDC demonstrates a certain level of competitiveness over DIM.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yating Shen, Lujie Zeng, and Ping Zong "Research on foreground subject segmentation algorithm with high similarity of foreground background", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030W (16 October 2023); https://doi.org/10.1117/12.3009537

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KEYWORDS

Image segmentation

Image processing algorithms and systems

Education and training

Convolution

Deformation

Data modeling

Feature extraction

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