Learning completed discriminative local features for texture classification
Zhang, Zhong1; Liu, Shuang1; Mei, Xing2; Xiao, Baihua3; Zheng, Liang4
Source PublicationPATTERN RECOGNITION
2017-07-01
Volume67Pages:263-275
SubtypeArticle
AbstractLocal 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.
KeywordTexture Classification Discriminative Learning Local Binary Patterns Adaptive Histogram Accumulation
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2017.02.021
WOS KeywordROTATION-INVARIANT FEATURES ; BINARY PATTERNS ; IMAGE CLASSIFICATION ; FACE RECOGNITION ; RADON-TRANSFORM ; RANDOM-FIELDS ; GRAY-SCALE ; SEGMENTATION ; RETRIEVAL ; FILTERS
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000399520700022
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15091
Collection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
Affiliation1.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
Recommended Citation
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.
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