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论文题目: |
Extraction of visual texture features of seabed sediments using an SVDD approach |
第一作者: |
Li Y(李岩);Liu SJ(刘世杰);Zhu PQ(祝普强);Yu JC(俞建成);Li S(李硕) |
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发表刊物: |
OCEAN ENGINEERING |
发表年度: |
2017 |
卷,期,页: |
142,,501-506 |
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论文摘要: |
Perception of the seabed environment is an important capability of autonomous underwater vehicles. This paper focuses on defining and extracting robust texture features from visual images that lead to useful and practical automated identification of the types of seabed sediments. The visual texture features are described by using a gray-level co-occurrence matrix (GLCM) and fractal dimension, after which an unsupervised learning method, self-organizing map (SOM), is adopted to evaluate the validity of features descriptors on three types of seabed sediments. Subsequently, a kernel-based approach that exhibits robustness versus low numbers of high dimensional samples, named support vector domain description (SVDD), is applied to classify the types of seabed sediments. In comparison with state-of-the-art classifiers, the experimental results demonstrated the effectiveness of the SVDD on the classification of seabed sediments. |
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