Abstract
Recently, the classification of surface textures is carried out using various modelling approaches. To analyse the surface texture, most of the techniques uses large amount of training data which adds up to considerable computational cost. However, the implementation of various neural network models also requires significant amount of training images to classify surface textures. In the proposed paper, a deep learning-based model is presented using convolution neural network (CNN). Further, this model is divided into two sub models knowing model-1 and model-2. The approach is designed with customized parameters configuration to classify surface texture using a smaller number of training samples. The image feature vectors are generated using statistical operations to compute the physical appearance of the surface and a CNN model is used to classify the generated surfaces with appropriate labels into classes. The Kylberg Texture dataset is used to evaluate the proposed models using 16 texture classes. The advantage of proposed models over pre-trained networks is that the entire models is customized according to specific training requirements. Further, to demonstrate the state-of-the-art results, the proposed approach is compared with other existing techniques. Our experimental results are better than the conventional techniques and achieves an accuracy of 92.42% for model-1 and 96.36% for model-2. In addition, the proposed models maintain balance between accuracy and computational cost.
Similar content being viewed by others
References
Aggarwal A, Rani A, Kumar M (2019) A robust method to authenticate license plates using segmentation and ROI based approach. Smart and Sustainable Built Environment, DOI: https://doi.org/10.1108/SASBE-07-2019-0083
Brownlee J (2019) Gentle introduction to the adam optimization algorithm for deep learning. Machine Learning Mastery Pty. Ltd. , 2019. [Online]. Available: https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/ss. [Accessed March 2019].
Chang HS, Kang K (2005) A compressed domain scheme for classifying block edge patterns. IEEE Trans Image Process 14(2):145–151
Chantler M, Petrou M, Penirsche A, Schmidt M, McGunnigle G (2005) Classifying surface texture while simultaneously estimating illumination direction. Int J Comput Vis 62(1–2):83–96
Chatra K, Kuppili V, Edla DR (2019) Texture image classification using deep neural network and binary dragon Fly optimization with a novel fitness function. Wirel Pers Commun 108(3):1513–1528
Chen L, Yang M (2017) Semi-supervised dictionary learning with label propagation for image classification. Computational Visual Media 3:83–94
Chen Z, Derakhshani RR, Halmen C, Kevern JT (2011) A texture-based method for classifying cracked concrete surfaces from digital images using neural networks. in The 2011 International Joint Conference on Neural Networks, San Jose
Cho M, Kim T, Kim IJ, Lee S (2020) Relational deep feature learning for heterogeneous face recognition. arXiv preprint arXiv:2003.00697
Chondronasios A, Popov A, Jordanov I (2016) Feature selection for surface defect classification of extruded aluminum profiles. Int J Adv Manuf Technol 83(1–4):33–41
Comer M, Delp E (1999) Segmentation of textured images using a multiresolution Gaussian autoregressive model. IEEE Trans Image Process 8(3):408–420
Dong Y, Zhang Z, Hong W-C (2018) A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting. Energies 11(4):1009
Ferreira A, Giraldi G (2017) Convolutional neural network approaches to granite tiles classification. Expert Syst Appl 84:1–17
Gibert X, Patel V, Chellappa R (2017) Deep multitask learning for railway track inspection. IEEE Trans Intell Transp Syst 18(1):153–164
Goyal V, Singh G, Tiwari O, Punia SK, Kumar M (2019) Intelligent skin Cancer detection Mobile application using convolution neural network. Journal of Advanced Research in Dynamical and Control Systems 11(7):253–259
Gu W, Lv Z, Hao M (2017) Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications volume 76, pages17719–17734(2017), vol. 76, pp. 17719–17734
Hanzaei SH, Afshar A, Barazandeh F (2017) Automatic detection and classification of the ceramic tiles’ surface defects. Pattern Recogn 66(2017):174–189
Hsu R-L, Mottaleb MA, Jain AK (2002) Face detection in color images. IEEE Trans Pattern Anal Mach Intell 24(5):696–706
Hu H, Li Y, Liu M, Liang W (2014) Classification of defects in steel strip surface based on multiclass support vector machine. Multimed Tools Appl 69(2014):199–216. https://doi.org/10.1007/s11042-012-1248-0
Huang Y, Wang Y, Tai Y, Liu X, Shen P, Li S, Li J, Huang F (2020) Curricularface: adaptive curriculum learning loss for deep face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5901-5910)
Khan AI, Wani MA (2018) Patch-based segmentation of latent fingerprint images using convolutional neural network. Appl Artif Intell, pp. 1–15
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems
Kumar A, Pang G (2002) Defect detection in textured materials using optimized filters. IEEE Transactions on System, Man, and Cybernetics–Part B,Cybernetics 32(5):553–570
Kumar M, Srivastava S (2018) Image authentication by assessing manipulations using illumination. Multimed Tools Appl 78(9):12451–11246
Kylberg G (n.d.) The kylberg texture dataset v. 1.0. In Centre for Image Analysis,Swedish University of Agricultural Sciences and Uppsala University,External report (Blue series) No. 35.Available online at: http://www.cb.uu.se/~gustaf/texture/.
Labati RD, Genovese A, Muñoz E, Piuri V, Scotti F (2017) A novel pore extraction method for heterogeneous fingerprint images using convolutional neural networks. Pattern Recogn Lett 113:58–66
Leung T, Malik J (2001) Representing and recognizing the visual appearance of materials. Int J Comput Vis 43(1):29–44
Liu C, Hu W (2019) Real-time geometric fitting and pose estimation for surface of revolution. Pattern Recogn 85(2019):90–108
Mäenpää T, Viertola J, Pietikäinen M (2003) Optimizing color and texture features for real-time visual. Pattern Anal Applic 6(3):169–175
Mallick-Goswami B, Datta A (2000) Detecting defects in fabric with laser-based morphological image. Text Res J 70:758–762
Park J-K, Kwon B-K, Park J-H, Kang D-J (2016) Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology 3(3):303–310
Shin B-S, Tao J, Klette R (2015) A superparticle filter for lane detection. Pattern Recogn 48(2015):3333–3345
Tao X, Zhang D, Ma W, Liu X, Xu D (2018) Automatic metallic surface defect detection and recognition with convolutional neural networks. Applied Sciences, vol. 8, no. 9
Thompson EM, Biasotti S (2018) Description and retrieval of geometric patterns on surface meshes using an e dge-base d LBP approach. Pattern Recogn 82(2018):1–15
Veerashetty S, Patil NB (2019) Novel LBP based texture descriptor for rotation, illumination and scale invariance for image texture analysis and classification using multi-kernel SVM. Multimed Tools Appl:1–21
Wu C-M, Chen Y-C, Hsieh K-S (1992) Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging 11(2):141–152
Xu Y, Ji H, Fermüller C (2009) Viewpoint invariant texture description using fractal analysis. Int J Comput Vis 83(1):85–100
YongHua X, Cong WJ (2015) Study on the identification of the wood surface defects based on texture features. Optik - International Journal for Light and Electron Optics 126(19):2231–2235
Zhang Z, Hong W-C (2019) Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dynamics 98(4):1107–1136
Zhang Z, Hong W-C, Li J (2020) Electric load forecasting by hybrid self-recurrent support vector regression model with Variational mode decomposition and improved cuckoo search algorithm. IEEE Access 8:14642–14658
Zhu Z, You X, Chen CP, Tao D, Ou W, Jiang X, Zou J (2015) An adaptive hybrid pattern for noise-robust texture analysis. PatternRecognition 48(2015):2592–2608
Author information
Authors and Affiliations
Corresponding author
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
Aggarwal, A., Kumar, M. Image surface texture analysis and classification using deep learning. Multimed Tools Appl 80, 1289–1309 (2021). https://doi.org/10.1007/s11042-020-09520-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09520-2