Texture Classification in Extreme Scale Variations Using GANet

doi:10.1109/TIP.2019.2903300

	Texture Classification in Extreme Scale Variations Using GANet
	Liu, Li 1,2; Chen, Jie 2,3; Zhao, Guoying 2; Fieguth, Paul 4; Chen, Xilin 5; Pietikainen, Matti 2
	2019-08-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	28 期号:8 页码:3910-3922
摘要	Research in texture recognition often concentrates on recognizing textures with intraclass variations, such as illumination, rotation, viewpoint, and small-scale changes. In contrast, in real-world applications, a change in scale can have a dramatic impact on texture appearance to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are among the hardest to handle. In this paper, we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a genetic algorithm to change the filters in the hidden layers during network training in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a Fisher vector pooling of a convolutional neural network filter bank feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding, we are developing a new dataset, the extreme scale variation textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms the gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing the superior performance compared with the state-of-the-art.
关键词	Texture descriptors rotation invariance local binary pattern (LBP) feature extraction texture analysis
DOI	10.1109/TIP.2019.2903300
收录类别	SCI
语种	英语
资助项目	Center for Machine Vision and Signal Analysis at the University of Oulu ; Tekes Fidipro Program[1849/31/2015] ; Business Finland Project[3116/31/2017] ; Infotech Oulu ; National Natural Science Foundation of China[61872379] ; Academy of Finland for Project MiGA[316765] ; ICT 2023 Project[313600] ; Project ICONICAL[313467] ; 6Genesis Flagship[318927]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000472609200004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4172
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Li
作者单位	1.Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China 2.Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland 3.Peng Chong Lab, Shenzhen 518055, Peoples R China 4.Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Li,Chen, Jie,Zhao, Guoying,et al. Texture Classification in Extreme Scale Variations Using GANet[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(8):3910-3922.
APA	Liu, Li,Chen, Jie,Zhao, Guoying,Fieguth, Paul,Chen, Xilin,&Pietikainen, Matti.(2019).Texture Classification in Extreme Scale Variations Using GANet.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(8),3910-3922.
MLA	Liu, Li,et al."Texture Classification in Extreme Scale Variations Using GANet".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.8(2019):3910-3922.