Superpixel segmentation with attention convolution neural network

Jingjing Wang; Zhenye Luan; Zishu Yu; Jinwen Ren; Wei Wei Yue; Jing Fang

doi:10.1117/12.2611692

11 November 2021 Superpixel segmentation with attention convolution neural network

Jingjing Wang, Zhenye Luan, Zishu Yu, Jinwen Ren, Wei Wei Yue, Jing Fang

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760D (2021) https://doi.org/10.1117/12.2611692
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

A superpixel consists of a series of small regions composed of pixel points that are located next to each other and have similar features such as color, luminance, and texture. Most of these small areas retain the original information of the image, which facilitates faster follow-up processing. Most of the existing superpixel segmentation methods do not use deep learning network architectures. Some methods use deep learning, but they also have a very simple network architecture. And there are a few superpixel segmentation methods combined with deep learning. In this method, a more complex convolutional neural network with an attention module is applied to superpixel segmentation. The resulting superpixel segmentation network is more complex, and the addition of the attention module allows for more accurate chunking of the images, thus yielding more comprehensive and detailed segmentation results. By experimenting on a public dataset BSDS500, the method has higher accuracy in superpixel segmentation. Also, the segmentation speed of the method is similar to that of the existing simple segmentation networks.

Citation Download Citation

Jingjing Wang, Zhenye Luan, Zishu Yu, Jinwen Ren, Wei Wei Yue, and Jing Fang "Superpixel segmentation with attention convolution neural network", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760D (11 November 2021); https://doi.org/10.1117/12.2611692

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