Semantic segmentation based on neural network and Bayesian network

Wenying Ge; Guoying Liu

doi:10.1117/12.2031464

26 October 2013 Semantic segmentation based on neural network and Bayesian network

Wenying Ge, Guoying Liu

Proceedings Volume 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis; 89170Z (2013) https://doi.org/10.1117/12.2031464
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

It is rather difficult for low-level visual features to describe the need for specific applications of image understanding, which results in the inconsistency between vision information and application need. In this paper, a new model is proposed to reduce this gap by combining low-level visual features with semantic features. It uses the output of neural network as the semantic feature, which is accompanied with the priori label features to describe the image after making normalization. And then, the proposed method employs Potts to model the distribution of label priori, and utilizes the Bayesian network to classify images. Several experiments on both synthetic and real images have verified that this method can get more accurate segmentation.

Citation Download Citation

Wenying Ge and Guoying Liu "Semantic segmentation based on neural network and Bayesian network", Proc. SPIE 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis, 89170Z (26 October 2013); https://doi.org/10.1117/12.2031464

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