Zehuan Yuan
Modeling spatial layout for scene image understanding via a novel multiscale sum-product network
Yuan, Zehuan; Wang, Hao; Wang, Limin; Lu, Tong; Palaiahnakote, Shivakumara; Lim Tan, Chew
Authors
Hao Wang
Limin Wang
Tong Lu
Dr Shivakumara Palaiahnakote S.Palaiahnakote@salford.ac.uk
Lecturer
Chew Lim Tan
Contributors
Z. Yuan
Other
H. Wang
Other
L. Wang
Other
T. Lu
Other
Dr Shivakumara Palaiahnakote S.Palaiahnakote@salford.ac.uk
Other
C. Lim Tan
Other
Abstract
Semantic image segmentation is challenging due to the large intra-class variations and the complex spatial layouts inside natural scenes. This paper investigates this problem by designing a new deep architecture, called multiscale sum-product network (MSPN), which utilizes multiscale unary potentials as the inputs and models the spatial layouts of image content in a hierarchical manner. That is, the proposed MSPN models the joint distribution of multiscale unary potentials and object classes instead of single unary potentials in popular settings. Besides, MSPN characterizes scene spatial layouts in a fine-to-coarse manner to enforce the consistency in labeling. Multiscale unary potentials at different scales can thus help overcome semantic ambiguities caused by only evaluating single local regions, while long-range spatial correlations can further refine image labeling. In addition, higher orders are able to pose the constraints among labels. By this way, multi-scale unary potentials, long-range spatial correlations, higher-order priors are well modeled under the uniform framework in MSPN. We conduct experiments on two challenging benchmarks consisting of the MSRC-21 dataset and the SIFT FLOW dataset. The results demonstrate the superior performance of our method comparing with the previous graphical models for understanding scene images.
Citation
Yuan, Z., Wang, H., Wang, L., Lu, T., Palaiahnakote, S., & Lim Tan, C. (2016). Modeling spatial layout for scene image understanding via a novel multiscale sum-product network. Expert systems with applications, https://doi.org/10.1016/j.eswa.2016.07.015
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 9, 2016 |
Publication Date | 2016 |
Deposit Date | Nov 15, 2024 |
Journal | Expert Systems with Applications |
Print ISSN | 0957-4174 |
Electronic ISSN | 1873-6793 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.1016/j.eswa.2016.07.015 |
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