Encoder-decoder with multi-scale information fusion for semantic image segmentation

Xinxin Ma; Kai Liu; Chongyang Ding; Lin Yan; Meiyu Duan

doi:10.1117/12.2557506

3 January 2020 Encoder-decoder with multi-scale information fusion for semantic image segmentation

Xinxin Ma, Kai Liu, Chongyang Ding, Lin Yan, Meiyu Duan

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731C (2020) https://doi.org/10.1117/12.2557506
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.

Citation Download Citation

Xinxin Ma, Kai Liu, Chongyang Ding, Lin Yan, and Meiyu Duan "Encoder-decoder with multi-scale information fusion for semantic image segmentation", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731C (3 January 2020); https://doi.org/10.1117/12.2557506

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KEYWORDS

Convolution

Computer programming

Image segmentation

Performance modeling

Data modeling

Information fusion

Nonlinear filtering

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