Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures
This paper presents a convolutional sparse coding (CSC)‐based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can ext...
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| Veröffentlicht in: | Computer-aided civil and infrastructure engineering Jg. 34; H. 8; S. 654 - 676 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Hoboken
Wiley Subscription Services, Inc
01.08.2019
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| ISSN: | 1093-9687, 1467-8667 |
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| Abstract | This paper presents a convolutional sparse coding (CSC)‐based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can extract visual features representing characteristics of target images because it can successfully estimate optimal convolutional dictionary filters and sparse features as visual features by training from a small number of distress images. The optimal dictionaries trained from distress images have basic components of visual characteristics such as edge and line information of distress images. Furthermore, sparse feature maps estimated on the basis of the dictionaries represent both strength of the basic components and location information of regions having their components, and these maps can represent distress images. That is, sparse feature maps can extract key components from distress images that have diverse visual characteristics. Therefore, CSC‐based feature extraction is effective for training from a limited number of distress images that have diverse visual characteristics. The construction of a novel neural network, CSDRN, by the use of a combination of CSC‐based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper. Accurate distress classification is realized via the CSDRN. |
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| AbstractList | This paper presents a convolutional sparse coding (CSC)‐based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can extract visual features representing characteristics of target images because it can successfully estimate optimal convolutional dictionary filters and sparse features as visual features by training from a small number of distress images. The optimal dictionaries trained from distress images have basic components of visual characteristics such as edge and line information of distress images. Furthermore, sparse feature maps estimated on the basis of the dictionaries represent both strength of the basic components and location information of regions having their components, and these maps can represent distress images. That is, sparse feature maps can extract key components from distress images that have diverse visual characteristics. Therefore, CSC‐based feature extraction is effective for training from a limited number of distress images that have diverse visual characteristics. The construction of a novel neural network, CSDRN, by the use of a combination of CSC‐based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper. Accurate distress classification is realized via the CSDRN. |
| Author | Takahashi, Sho Ogawa, Takahiro Haseyama, Miki Maeda, Keisuke |
| Author_xml | – sequence: 1 givenname: Keisuke surname: Maeda fullname: Maeda, Keisuke organization: Hokkaido University – sequence: 2 givenname: Sho surname: Takahashi fullname: Takahashi, Sho organization: Hokkaido University – sequence: 3 givenname: Takahiro surname: Ogawa fullname: Ogawa, Takahiro email: ogawa@lmd.ist.hokudai.ac.jp organization: Hokkaido University – sequence: 4 givenname: Miki surname: Haseyama fullname: Haseyama, Miki organization: Hokkaido University |
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| Title | Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures |
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