Abstract
Local Binary Patterns (LBP) is an texture feature, which is widely used in texture discrimination, face recognition, painting classification and other fields. The previous LBP feature extraction methods and many improved algorithms are all based on the gray scale image. Because there is information loss during the conversion from color image to gray image, the LBP methods need combine with other color features to improve the accuracy of classification. In this paper, a LBP encoding method for color image based on color space distance is proposed, which can not only directly achieve LBP feature from color image, but also can be applied to improve various previous methods. The experiment shows that color-based LBP method can filter background information better. With three different classifiers, the accuracy of classification which only used color-based LBP features is about 15% higher than that of existing LBP features. Finally, the application effect in local binary patterns histograms (LBPH) with color-based LBP further proves the advantage.
Similar content being viewed by others
References
Alamgir KCEA (2018) Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos. Fire Safety J 102:1–10
Audrey L, Losson O, Ludovic M (2016) Color local binary patterns: Compact descriptors for texture classification. J Electron Imaging 25:061404
Chandana P, Srinivas Y (2015) Comparison between local binary pattern and chain code techniques for image retrieval using sketches. Int J Adv Res Comput Commun Eng 4:671–675
Das R, Bhattacharyya S (2019) Data augmentation and feature fusion for melanoma detection with content based image classification. In: AMLTA 2019, pp 712–721
Di Huang MAEA (2011) Local binary patterns and its application to facial image analysis: A survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41:765–781
Faridah Y, Nicolay S et al (2018) Lbp vs svd for color images feature extraction. In: IEEE International Conference on Smart Instrumentation, Measurement and Application, pp 1–6
Fu Y, Wu T et al (2020) Advanced medical imaging analytics in breast cancer diagnosis. In: 2019 IEEE International Conference on Image Processing (ICIP), pp 301–319
Gianluigi CC, Raimondo S (2015) Image orientation detection using lbp-based features and logistic regression. Multimed Tools Appl 74:3013–3034
Guarnera F, Giudice O et al (2019) A new study on wood fibers textures: Documents authentication through lbp fingerprint. In: 2019 IEEE International Conference on Image Processing (ICIP), pp 4594–4598
Hussain N et al (2020) A deep neural network and classical features based scheme for objects recognition: an application for machine inspection. Multimed Tools Appl 02:1–23
Khammari M (2019) Robust face anti-spoofing using cnn with lbp and wld. IET Image Process 13:1880–1884
Lan R (2020) An lbp encoding scheme jointly using quaternionic representation and angular information. Neural Comput Appl 32:4317–4323
Liu P, Prasetyo H (2017) Fusion of color histogram and lbp-based features for texture image retrieval and classification. Inf Sci 390:95–111
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110
Maryam Nisa SK et al (2020) Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features. Appl Sci 10:4966
Mufarroha FA, Anamisa DR, Hapsani AG (2020) Content based image retrieval using two color feature extraction. In: International Conference on Science and Technology 2019, pp 1–6
Muhammad Rashid MA et al (2020) A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection. Sustainability 12:1–24
Nazari MR, Fatemizadeh E (2010) A cbir system for human brain magnetic resonance image indexing. Int J Comput Appl 7:33–37
Nishant S, Vipin T (2016) An integrated approach for image retrieval using local binary pattern. Multimed Tools Appl 75:6569–6583
Nithin PB, Chemmanam AJ et al (2019) Face tracking robot testbed for performance assessment of machine learning techniques. In: 2019 7th International Conference on Smart Computing & Communications (ICSCC), pp 1–5
Obulesu A, Kumar VV, Sumalatha L, Niranjan SK (2017) Region based image retrieval using ranking concept of local binary pattern. In: 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp 352–359
Ojala T, Pietikainen M., Maenpaa T. (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987
Pawar MP, Belagali PP (2015) Image retrieval technique using local binary pattern (lbp). Int J Sci Res 4:1440–1443
Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using svm classification and relevance feedback. IEEE Trans Inf Technol Biomed Publ IEEE Eng Med Biol Soc 15:640–646
Rashid M et al (2019) Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and sift point features. Multimed Tools Appl 78:15751–15777
Rashid M et al (2019) On the influence of the color model for image boundary detection algorithms based on convolutional neural networks, pp 1–8
Rik D, Ekta W (2019) Partition selection with sparse autoencoders for content based image classification. Neural Comput Appl 31:675–690
Sachinkumar V, Patil NB (2020) Novel lbp based texture descriptor for rotation, illumination and scale invariance for image texture analysis and classification using multi-kernel svm. Multimed Tools Appl 79:9935–9955
Saleh SA, Yeo KC et al (2017) An improved face recognition method using local binary pattern method. In: 2017 11th International Conference on Intelligent Systems and Control (ISCO), pp 112–118
Satpathy Amit JX, Lung EH (2014) Lbp-based edge-texture features for object recognition. IEEE Trans Image Process 24:1953–1964
Shengcai Liao ZL et al (2007) Learning multi-scale block local binary patterns for face recognition. In: International Conference on Advances in Biometrics, pp 828–837
Singh H, Agrawal D (2017) An analysis based on local binary pattern (lbp) and color moment (cm) for efficient image retrieval. In: 2016 International Conference on Emerging Technological Trends (ICETT), pp 1–6
Singh C, Walia E, Kaura KP (2018) Color texture description with novel local binary patterns for effective image retrieval. Pattern Recogn 76:50–68
Sotoodeh R (2019) A novel adaptive lbp-based descriptor for color image retrieval. Expert Syst Appl 127:342–352
Sotoodeh M, Boostani R (2019) A novel adaptive lbp-based descriptor for color image retrieval. Expert Syst Appl 127:342–352
Toreini Ehsan SSF, Hao F (2017) Texture to the rescue: Practical paper fingerprinting based on texture patterns. ACM Trans Privacy Secur 20:1–29
X Liao XZ et al (2020) Robust detection of image operator chain with two-stream convolutional neural network 14:955–968
Xin Liao MC et al (2020) Adaptive payload distribution in multiple images steganography based on image texture features. IEEE Trans Depend Sec Comput PP:1–1
Youngjun L, Mo J, Hangbyung C (2018) Development of robust validation method through driverless test for vision-based oncoming vehicle collision avoidance system. In: 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp 564–569
Zeebaree DQ, Abdulazeez AM et al (2019) Trainable model based on new uniform lbp feature to identify the risk of the breast cancer. In: 2019 International Conference on Advanced Science and Engineering (ICOASE), pp 106–111
Funding
This study was not funded by any project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from hmoe@vip.qq.com
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhao, Q. Research on the application of local binary patterns based on color distance in image classification. Multimed Tools Appl 80, 27279–27298 (2021). https://doi.org/10.1007/s11042-021-10996-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-10996-9