Texture Representation via Joint Statistics of Local Quantized Patterns

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概要

• 論文の詳細を見る

• In this paper, a simple yet efficient texture representation is proposed for texture classification by exploring the joint statistics of local quantized patterns (jsLQP). In order to combine information of different domains, the Gaussian derivative filters are first employed to obtain the multi-scale gradient responses. Then, three feature maps are generated by encoding the local quantized binary and ternary patterns in the image space and the gradient space. Finally, these feature maps are hybridly encoded, and their joint histogram is used as the final texture representation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art LBP based and even learning based methods for texture classification.

著者

• Huang Chao

  School Of Health And Sports Sciences East China Normal University

• XU Linfeng

  School of Electronic Engineering, University of Electronic Science and Technology of China

• LUO Bing

  School of Electronic Engineering, University of Electronic Science and Technology of China

• SONG Tiecheng

  School of Electronic Engineering, University of Electronic Science and Technology of China

• HUANG Chao

  School of Electronic Engineering, University of Electronic Science and Technology of China

関連論文

• Associations of Physique with the Socioeconomic Factors of Family and Regional Origin in Chinese University Students
• Texture Representation via Joint Statistics of Local Quantized Patterns

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