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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)
作者部门水下机器人研究室
会议名称OCEANS 2016 - Shanghai
会议日期April 10-13, 2016
会议地点Shanghai, China
会议录名称OCEANS 2016 - Shanghai
出版者IEEE
出版地Piscataway, NJ, USA
2016
页码1-5
收录类别EI ; CPCI(ISTP)
EI收录号20162902613181
WOS记录号WOS:000386521800307
产权排序1
ISSN号0197-7385
ISBN号978-1-4673-9724-7
摘要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.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/18777
专题水下机器人研究室
通讯作者Song SM(宋三明)
作者单位Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
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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.
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