Learning completed discriminative local features for texture classification

doi:10.1016/j.patcog.2017.02.021

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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团队

	Learning completed discriminative local features for texture classification
	Zhang, Zhong 1; Liu, Shuang 1; Mei, Xing2 ; Xiao, Baihua3 ; Zheng, Liang 4
刊名	PATTERN RECOGNITION
	2017-07-01
卷号	67 页码:263-275
关键词	Texture Classification Discriminative Learning Local Binary Patterns Adaptive Histogram Accumulation
DOI	10.1016/j.patcog.2017.02.021
文献子类	Article
英文摘要	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.
WOS关键词	ROTATION-INVARIANT FEATURES ; BINARY PATTERNS ; IMAGE CLASSIFICATION ; FACE RECOGNITION ; RADON-TRANSFORM ; RANDOM-FIELDS ; GRAY-SCALE ; SEGMENTATION ; RETRIEVAL ; FILTERS
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000399520700022
资助机构	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)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15091]
专题	自动化研究所_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团队
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.