期刊
APPLIED SOFT COMPUTING
卷 80, 期 -, 页码 57-79出版社
ELSEVIER
DOI: 10.1016/j.asoc.2019.03.017
关键词
Image attribute annotation; Material attribute classification; Effective range-based gene selection; eXtreme gradient boosting; Relative attribute
资金
- National Natural Science Foundation of China [61762038, 61741108, 61861016]
- Natural Science Foundation of Jiangxi Province [20171BAB202023]
- Key Research and Development Plan of Jiangxi Provincial Science and Technology Department [20171BBG70093]
- Science and Technology Projects of Jiangxi Provincial Department of Education [GJJ160531]
- Humanity and Social Science Foundation of the Ministry of Education [17YJAZH117, 16YJAZH029]
- Humanity and Social Science Foundation of the Jiangxi Province [16TQ02]
Material attribute recognition from the visual appearance of attributes is an important problem in computer vision field. However, few works model the hierarchical relationship between material attributes and their deep-level semantics that occurs in the same image. Meanwhile, single image feature is insufficient to achieve high-quality material attribute classification. In this paper, we present methods for generating a new hierarchical material attribute representation mechanism using a new-designed feature mid-fusion algorithm and the state-of-the-art relative attribute (RA) model. The novel feature mid-fusion algorithm can improve the performance of material attribute classification. The deep-level semantics of material attributes are mined by the state-of-the-art RA model. They provide considerable useful and detailed knowledge on material attributes. We call the novel feature mid-fusion algorithm gene selection eXtreme gradient boosting (GS-XGBoost). This algorithm considers the state-of-the-art boosting idea (eXtreme gradient boosting) and the popular multi-feature fusion idea (effective range-based gene selection). To comprehensively describe material attributes, we also measure the relative degree of their deep-level semantics. A new hierarchical material attribute representation mechanism is constructed on the basis of the correctly classified material attributes and their deep-level semantics. The mechanism has two forms. One is binary attribute representation mechanism, and the other is relative attribute representation mechanism. We demonstrate the effectiveness of the proposed GS-XGBoost algorithm on two different datasets. The proposed GS-XGBoost algorithm is not an end-to-end framework but is efficient and practical for fine- and coarse-grained material attribute classification problems that can be applied in different scenarios in large-scale product image retrieval, robotics, and industrial inspection. The novel hierarchical material attribute representation mechanism will help humans or robotics accurately recognize diverse materials and their deep-level semantics. Our research contributes to not only computer science but also material science and engineering. (C) 2019 Elsevier B.V. All rights reserved.
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