Learning completed discriminative local features for texture classification

doi:10.1016/j.patcog.2017.02.021

CASIA OpenIR > 复杂系统管理与控制国家重点实验室 > 影像分析与机器视觉

	Learning completed discriminative local features for texture classification
	Zhang, Zhong 1; Liu, Shuang 1; Mei, Xing2 ; Xiao, Baihua3 ; Zheng, Liang 4
发表期刊	PATTERN RECOGNITION
	2017-07-01
卷号	67 页码:263-275
文章类型	Article
摘要	Local binary patterns (LBP) and its variants have shown great potentials in texture classification tasks. LBP-like texture classification methods usually follow a two-step feature extraction process: in the first pattern encoding step, the local structure information around each pixel is encoded into a binary string; in the second histogram accumulation step, the binary strings are accumulated into a histogram as the feature vector of a texture image. The performances of these classification methods are closely related to the distinctiveness of the feature vectors. In this paper, we propose a novel feature representation method, namely Completed Discriminative Local Features (CDLF), for texture classification. The proposed CDLF improves the distinctiveness of LBP-like feature vectors in two aspects: in the pattern encoding stage, we learn a transformation matrix using labeled data, which significantly increases the discrimination power of the encoded binary strings; in the histogram accumulation step, we use an adaptive weight strategy to consider the contributions of pixels in different regions. The experimental results on three challenging texture databases demonstrate that the proposed CDLF achieves significantly better results than previous LBP-like feature representation methods for texture classification tasks. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	Texture Classification Discriminative Learning Local Binary Patterns Adaptive Histogram Accumulation
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patcog.2017.02.021
关键词[WOS]	ROTATION-INVARIANT FEATURES ; BINARY PATTERNS ; IMAGE CLASSIFICATION ; FACE RECOGNITION ; RADON-TRANSFORM ; RANDOM-FIELDS ; GRAY-SCALE ; SEGMENTATION ; RETRIEVAL ; FILTERS
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61401309 ; Natural Science Foundation of Tianjin(15JCQNJC01700) ; Doctoral Fund of Tianjin Normal University(5RL134 ; Open Projects Program of National Laboratory of Pattern Recognition(201700001) ; 61501327 ; 52X61405) ; 61401310)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000399520700022
引用统计	被引频次：23[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15091
专题	复杂系统管理与控制国家重点实验室_影像分析与机器视觉
作者单位	1.Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin 300387, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Intelligent Control Co, Beijing 100190, Peoples R China 4.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia
推荐引用方式 GB/T 7714	Zhang, Zhong,Liu, Shuang,Mei, Xing,et al. Learning completed discriminative local features for texture classification[J]. PATTERN RECOGNITION,2017,67:263-275.
APA	Zhang, Zhong,Liu, Shuang,Mei, Xing,Xiao, Baihua,&Zheng, Liang.(2017).Learning completed discriminative local features for texture classification.PATTERN RECOGNITION,67,263-275.
MLA	Zhang, Zhong,et al."Learning completed discriminative local features for texture classification".PATTERN RECOGNITION 67(2017):263-275.