Convolutional Neural Network approaches to granite tiles classification

Abstract The quality control process in stone industry is a challenging problem to deal with nowadays. Due to the similar visual appearance of different rocks with the same mineralogical content, economical losses can happen in industry if clients cannot recognize properly the rocks delivered as the ones initially purchased. In this paper, we go toward the automation of rock-quality assessment in different image resolutions by proposing the first data-driven technique applied to granite tiles classification. Our approach understands intrinsic patterns in small image patches through the use of Convolutional Neural Networks tailored for this problem. Experiments comparing the proposed approach to texture descriptors in a well-known dataset show the effectiveness of the proposed method and its suitability for applications in some uncontrolled conditions, such as classifying granite tiles under different image resolutions.

[1]  Francesco Bianconi,et al.  Dominant local binary patterns for texture classification: Labelled or unlabelled? , 2015, Pattern Recognit. Lett..

[2]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[3]  Ari Visa,et al.  Rock image classification using color features in Gabor space , 2005, J. Electronic Imaging.

[4]  Francesco Bianconi,et al.  General Framework for Rotation Invariant Texture Classification Through Co-occurrence of Patterns , 2014, Journal of Mathematical Imaging and Vision.

[5]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[6]  F. Álvarez.  GRANITE TEXTURE CLASSIFICATION WITH GABOR FILTERS , 2006 .

[7]  Paul F. Whelan,et al.  Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification , 2011, Machine Vision and Applications.

[8]  Anderson Rocha,et al.  Toward Open Set Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Javier Martínez,et al.  Identification of Granite Varieties from Colour Spectrum Data , 2010, Sensors.

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[12]  Francesco Bianconi,et al.  Automatic classification of granite tiles through colour and texture features , 2012, Expert Syst. Appl..

[13]  Li Fei-Fei,et al.  DenseCap: Fully Convolutional Localization Networks for Dense Captioning , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[15]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[16]  Shervan Fekri Ershad,et al.  Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features , 2011, ArXiv.

[17]  Francesco Bianconi,et al.  On Comparing Colour Spaces From a Performance Perspective: Application to Automated Classification of Polished Natural Stones , 2015, ICIAP Workshops.

[18]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[19]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[20]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[21]  Anderson Rocha,et al.  Laser printer attribution: exploring new features and beyond. , 2015, Forensic science international.

[22]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .