Multi-scale Joint Encoding of Local Binary Patterns for Texture and Material Classification
文献类型：会议论文
| 作者 | Xianbiao Qi; Yu Qiao; Chun-Guang Li; Jun Guo |
| 出版日期 | 2013 |
| 会议名称 | 2013 24th British Machine Vision Conference, BMVC 2013 |
| 会议地点 | Bristol, United kingdom |
| 英文摘要 | In the current multi-scale LBP (MS-LBP) on texture and material classification, each scale is encoded into histograms individually. This strategy ignores the correlation between different scales, and loses a lot of discriminative information. In this paper, we propose a novel and effective multi-scale joint encoding oflocal binary patterns (MSJ-LBP) for texture and material classification. In MSJ-LBP, the joint encoding strategy can capture the correlation between different scales and hence depict richer local structures. In addition, the proposed MSJ-LBP is computationally simple and rotation invariant. Extensive experiments on four challenging databases (Outex_TC_00012, Brodatz, KTH-TIPS, KTH-TIPS2a) show that the proposed MSJ-LBP significantly outperforms the classical MS-LBP and achieves the state-of-the-art performance. |
| 收录类别 | EI |
| 语种 | 中文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4486]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Xianbiao Qi,Yu Qiao,Chun-Guang Li,et al. Multi-scale Joint Encoding of Local Binary Patterns for Texture and Material Classification[C]. 见:2013 24th British Machine Vision Conference, BMVC 2013. Bristol, United kingdom. |
入库方式： OAI收割
来源：深圳先进技术研究院
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