Survey on Recent Progresses of Semantic Image Segmentation with CNNs

Convolutional neural networks (CNNs) have been the mainstream in many computer vision tasks, such as image classification, object detection, face recognition and so on. We survey the state-of-the-art results on Pascal VOC 2012 semantic segmentation challenge which has made great progresses in 2015....

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Bibliographic Details
Published in2016 International Conference on Virtual Reality and Visualization (ICVRV) pp. 158 - 163
Main Authors Qichuan Geng, Zhong Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
CNN
Computer architecture
Feature extraction
Image segmentation
Message passing
Neural networks
Pascal VOC 2012 challenge
Semantic image segmentation
Semantics
Training
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Summary:Convolutional neural networks (CNNs) have been the mainstream in many computer vision tasks, such as image classification, object detection, face recognition and so on. We survey the state-of-the-art results on Pascal VOC 2012 semantic segmentation challenge which has made great progresses in 2015. We investigate the effectiveness of the new layers, structures and strategies behind these results proposed to produce more refined segmentation. Their main contributions focus on utilizing more structures and contextual information in the image or feature spaces. Most of these approaches serve for several independent stages in semantic image segmentation. In this paper, we discuss possible architectures to incorporate existing structures and strategies. Finally possible directions on enhancing CNNs to segment given semantic objects are proposed.
DOI:10.1109/ICVRV.2016.34