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2018 | OriginalPaper | Chapter

Semantic Segmentation by Integrating Classifiers for Different Difficulty Levels

Authors : Daisuke Matsuzuki, Kazuhiro Hotta

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

Semantic segmentation assigns class labels to all pixels in an input image. In general, when the number of classes is large or when the appearance of each class frequency changes, the segmentation accuracy decreases drastically. In this paper, we propose to divide a classification task into sub-tasks according to the difficulty of classes. Our proposed method consists of 2 parts; training a network for each sub-task and training an integration network. Difficulty level depends on the number of pixels. By training the network for each difficulty level, we obtain probability maps for each sub-task. Then we train the integration network from those maps. In experiments, we evaluate the segmentation accuracy on the CamVid dataset which contains 11 classes. We divide all classes to 3 classes; easy, normal, and difficult classes. We compared our method with conventional method using all classes. We confirmed that the proposed method outperformed the conventional method.

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Title: Semantic Segmentation by Integrating Classifiers for Different Difficulty Levels
Authors: Daisuke Matsuzuki
Kazuhiro Hotta
Publisher: Springer International Publishing
Book: Advances in Visual Computing
Print ISBN: 978-3-030-03800-7

Electronic ISBN: 978-3-030-03801-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03801-4_53

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