HDPA: Hierarchical deep probability analysis for scene parsing

Yuan Yuan, Zhiyu Jiang, Qi Wangm

科研成果: 书/报告/会议事项章节会议稿件同行评审

5 引用 (Scopus)

摘要

Scene parsing is an important task in computer vision and many issues still need to be solved. One problem is about the non-unified framework for predicting things and stuff and the other one refers to the inadequate description of contextual information. In this paper, we address these issues by proposing a Hierarchical Deep Probability Analysis(HDPA) method which particularly exploits the power of probabilistic graphical model and deep convolutional neural network on pixel-level scene parsing. To be specific, an input image is initially segmented and represented through a CNN framework under Gaussian pyramid. Then the graphical models are built under each scale and the labels are ultimately predicted by structural analysis. Three contributions are claimed: unified framework for scene labeling, hierarchical probabilistic graphical modeling and adequate contextual information consideration. Experiments on three benchmarks show that the proposed method outperforms the state-of-the-arts in scene parsing.

源语言英语
主期刊名2017 IEEE International Conference on Multimedia and Expo, ICME 2017
出版商IEEE Computer Society
313-318
页数6
ISBN(电子版)9781509060672
DOI
出版状态已出版 - 28 8月 2017
活动2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, 香港
期限: 10 7月 201714 7月 2017

出版系列

姓名Proceedings - IEEE International Conference on Multimedia and Expo
ISSN(印刷版)1945-7871
ISSN(电子版)1945-788X

会议

会议2017 IEEE International Conference on Multimedia and Expo, ICME 2017
国家/地区香港
Hong Kong
时期10/07/1714/07/17

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