| Label field initialization for MRF-based sonar image segmentation by selective autoencoding | |
Song SM(宋三明) ; Si BL(斯白露); Feng XS(封锡盛); Liu KZ(刘开周)
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| Department | 水下机器人研究室 |
| Conference Name | OCEANS 2016 - Shanghai |
| Conference Date | April 10-13, 2016 |
| Conference Place | Shanghai, China |
| Source Publication | OCEANS 2016 - Shanghai |
| Publisher | IEEE |
| Publication Place | Piscataway, NJ, USA |
| 2016 | |
| Pages | 1-5 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20162902613181 |
| WOS ID | WOS:000386521800307 |
| Contribution Rank | 1 |
| ISSN | 0197-7385 |
| ISBN | 978-1-4673-9724-7 |
| Abstract | The optimal solution of a Markov random field (MRF) can be solved by constructing a Markov chain that eventually goes to a balance state. However, in most situations, only an suboptimal solution can be obtained, because it is hard to choose the ideal initial state and the updating strategy. While the updating strategy has been extensively investigated, the initialization issue has been fully neglected. Though k-means-clustering has been used exclusively in initializing the label field, it suffers from the lack of account of the local constraints, which is the most essential part of the MRF model. A structural method based on selective autoencoding (SAE) is proposed for the label field initialization of MRF model in the task of sonar image segmentation. SAE is similar to the AutoEncoder, with the largest difference on the activation function, where a piece-wise sigmoid activation function with two different slop parameters is used to selectively encode image patches that resemble shadow ares or other areas. The synapse matrixes of SAE network act as information filters, preserve specific area adaptively and selectively, generating a label field that is much closer to the balance state. Experiments on sonar image segmentation demonstrate the efficiency of the SAE algorithm. |
| Language | 英语 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/18777 |
| Collection | 水下机器人研究室 |
| Corresponding Author | Song SM(宋三明) |
| Affiliation | Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China |
| Recommended Citation GB/T 7714 | Song SM,Si BL,Feng XS,et al. Label field initialization for MRF-based sonar image segmentation by selective autoencoding[C]. Piscataway, NJ, USA:IEEE,2016:1-5. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Label field initiali（927KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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