Tree trunk texture classification using multi-scale statistical macro binary patterns and CNN
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Safia Boudra, Itheri Yahiaoui, and Ali BehloulAuthors Info & Claims
Safia Boudra, Itheri Yahiaoui, and Ali BehloulAuthors Info & ClaimsReferences
[1]
P. Belhumeur, D. Chen, S. Feiner, D. Jacobs, W. Kress, H. Ling, I. Lopez, R. Ramamoorthi, S. Sheorey, S. White, L. Zhang, Searching the world’s herbaria: A system for visual identification of plant species, in: European Conference on Computer Vision, ECCV, 2008, pp. 116–129.
[2]
Cope J.S., Corney D., Clark J.Y., Remagnino P., Wilkin P., Plant species identification using digital morphometrics: A review, Expert Syst. Appl. 39 (8) (2012) 7562–7573.
[3]
Mouine S., Yahiaoui I., Verroust-Blondet A., A shape-based approach for leaf classification using multiscaletriangular representation, in: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, ACM, 2013, pp. 127–134.
[4]
Mouine S., Yahiaoui I., Verroust-Blondet A., Plant species recognition using spatial correlation between the leaf margin and the leaf salient points, in: ICIP 2013-IEEE International Conference on Image Processing, IEEE, 2013.
[5]
Mzoughi O., Yahiaoui I., Boujemaa N., Zagrouba E., Semantic-based automatic structuring of leaf images for advanced plant species identification, Multimedia Tools Appl. 75 (3) (2016) 1615–1646.
[6]
Nilsback M.-E., Zisserman A., Delving deeper into the whorl of flower segmentation, Image Vis. Comput. (2009).
[7]
Seeland M., Rzanny M., Alaqraa N., Wäldchen J., Mäder P., Plant species classification using flower images—A comparative study of local feature representations, PLoS One 12 (2) (2017).
[8]
Wendel A., Sternig S., Godec M., Automated identification of tree species from images of the bark, leaves and needles, in: 16th Computer Vision Winter Workshop, Citeseer, 2011, p. 67.
[9]
Šulc M., Matas J., Fine-grained recognition of plants from images, Plant Methods 13 (1) (2017) 115.
[10]
Remeš V., Haindl M., Rotationally invariant bark recognition, in: Bai X., Hancock E.R., Ho T.K., Wilson R.C., Biggio B., Robles-Kelly A. (Eds.), Structural, Syntactic, and Statistical Pattern Recognition, Springer International Publishing, Cham, 2018, pp. 22–31.
[11]
S. Boudra, I. Yahiaoui, A. Behloul, Plant identification from bark: A texture description based on statistical macro binary pattern, in: 2018 24th International Conference on Pattern Recognition, ICPR, 2018, pp. 1530–1535.
[12]
Boudra S., Yahiaoui I., Behloul A., Statistical radial binary patterns (SRBP) for bark texture identification, in: International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, 2017, pp. 101–113.
[13]
S. Boudra, I. Yahiaoui, A. Behloul, Bark identification using improved statistical radial binary patterns, in: 2018 International Conference on Content-Based Multimedia Indexing, CBMI, 2018, pp. 1–6.
[14]
S. Bertrand, G. Cerutti, L. Tougne, Bark recognition to improve leaf-based classification in didactic tree species identification, in: VISAPP 2017-12th International Conference on Computer Vision Theory and Applications, 2017.
[15]
R. Ratajczak, S. Bertrand, C. Crispim-Junior, L. Tougne, Efficient bark recognition in the wild, in: International Conference on Computer Vision Theory and Applications, VISAPP 2019, 2019.
[16]
Wäldchen J., Mäder P., Plant species identification using computer vision techniques: A systematic literature review, Arch. Comput. Methods Eng. 25 (2) (2018) 507–543.
[17]
Cerutti G., Tougne L., Mille J., Vacavant A., Coquin D., Understanding leaves in natural images–A model-based approach for tree species identification, Comput. Vis. Image Underst. 117 (10) (2013) 1482–1501.
[18]
H. Goëau, P. Bonnet, A. Joly, N. Boujemaa, D. Barthelemy, J.-F. Molino, P. Birnbaum, E. Mouysset, M. Picard, The ImageCLEF 2011 plant images classi cation task, in: ImageCLEF 2011, 2011.
[19]
Mouine S., Yahiaoui I., Verroust-Blondet A., Joyeux L., Selmi S., Goëau H., An android application for leaf-based plant identification, in: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, ACM, 2013, pp. 309–310.
[20]
N. Kumar, P.N. Belhumeur, A. Biswas, D.W. Jacobs, W.J. Kress, I. Lopez, J.V.B. Soares, Leafsnap: A computer vision system for automatic plant species identification, in: The 12th European Conference on Computer Vision, ECCV, 2012.
[21]
Sixta T., Image and Video-Based Recognition of Natural Objects, (Ph.D. thesis) Diploma Thesis. Czech Technical University in Prague. Faculty of Electrical Engineering, 2011.
[22]
Le-Viet T., Hoang V.T., Local binary pattern based on image gradient for bark image classification, Tenth International Conference on Signal Processing Systems, vol. 11071, International Society for Optics and Photonics, 2019, p. 110710P.
[23]
Fekri-Ershad S., Bark texture classification using improved local ternary patterns and multilayer neural network, Expert Syst. Appl. 158 (2020).
[24]
Wojtech M., Bark: A Field Guide to Trees of the Northeast, University Press of New England, 2011, URL: https://books.google.fr/books?id=OSWqcQAACAAJ.
[25]
Wan Y.-Y., Du J.-X., Huang D.-S., Chi Z., Cheung Y.-M., Wang X.-F., Zhang G.-J., Bark texture feature extraction based on statistical texture analysis, in: Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, IEEE, 2004, pp. 482–485.
[26]
Song J., Chi Z., Liu J., Fu H., Bark classification by combining grayscale and binary texture features, in: Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, IEEE, 2004, pp. 450–453.
[27]
Boudra S., Yahiaoui I., Behloul A., A comparison of multi-scale local binary pattern variants for bark image retrieval, in: International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, 2015, pp. 764–775.
[28]
Šulc M., Matas J., Kernel-mapped histograms of multi-scale lbps for tree bark recognition, in: Image Vis. Comput. New Zealand, IVCNZ, 2013 28th International Conference of, IEEE, 2013, pp. 82–87.
[29]
Ojala T., Pietikainen M., Maenpaa T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell. 24 (7) (2002) 971–987.
[30]
Blaanco L.J., Travieso C.M., Quinteiro J.M., Hernandez P.V., Dutta M.K., Singh A., A bark recognition algorithm for plant classification using a least square support vector machine, in: Contemporary Computing, IC3, 2016 Ninth International Conference on, IEEE, 2016, pp. 1–5.
[31]
Chi Z., Houqiang L., Chao W., Plant species recognition based on bark patterns using novel Gabor filter banks, Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on, vol. 2, IEEE, 2003, pp. 1035–1038.
[32]
Huang Z.-K., Bark classification using RBPNN based on both color and texture feature, Int. J. Comput. Sci. Netw. Secur. 6 (11) (2006) 100–103.
[33]
Zhang J., Marszałek M., Lazebnik S., Schmid C., Local features and kernels for classification of texture and object categories: A comprehensive study, Int. J. Comput. Vis. 73 (2) (2007) 213–238.
[34]
Mäenpää T., Pietikäinen M., Multi-scale binary patterns for texture analysis, in: Scandinavian Conference on Image Analysis, Springer, 2003, pp. 885–892.
[35]
Guo Z., Zhang L., Zhang D., Rotation invariant texture classification using LBP variance (LBPV) with global matching, Pattern Recognit. 43 (3) (2010) 706–719.
[36]
Guo Z., Zhang L., Zhang D., A completed modeling of local binary pattern operator for texture classification, IEEE Trans. Image Process. 19 (6) (2010) 1657–1663.
[37]
Wang K., Bichot C.-E., Zhu C., Li B., Pixel to patch sampling structure and local neighboring intensity relationship patterns for texture classification, IEEE Signal Process. Lett. 20 (9) (2013) 853–856.
[38]
Liu L., Lao S., Fieguth P.W., Guo Y., Wang X., Pietikäinen M., Median robust extended local binary pattern for texture classification, IEEE Trans. Image Process. 25 (3) (2016) 1368–1381.
[39]
Alpaslan N., Hanbay K., Multi-resolution intrinsic texture geometry-based local binary pattern for texture classification, IEEE Access 8 (2020) 54415–54430.
[40]
Nguyen T.P., Vu N.-S., Manzanera A., Statistical binary patterns for rotational invariant texture classification, Neurocomputing 173 (2016) 1565–1577.
[41]
Hu Y., Wang Z., AlRegib G., Texture classification using block intensity and gradient difference (BIGD) descriptor, Signal Process., Image Commun. 83 (2020).
[42]
Liu L., Fieguth P., Guo Y., Wang X., Pietikäinen M., Local binary features for texture classification: Taxonomy and experimental study, Pattern Recognit. 62 (2017) 135–160.
[43]
Huang D., Shan C., Ardabilian M., Wang Y., Chen L., Local binary patterns and its application to facial image analysis: A survey, IEEE Trans. Syst. Man Cybern. C 41 (6) (2011) 765–781.
[44]
Nanni L., Lumini A., Brahnam S., Survey on LBP based texture descriptors for image classification, Expert Syst. Appl. 39 (3) (2012) 3634–3641.
[45]
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.
[46]
Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.
[47]
Carpentier M., Giguere P., Gaudreault J., Tree species identification from bark images using convolutional neural networks, in: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, IEEE, 2018, pp. 1075–1081.
[48]
Liao S., Zhu X., Lei Z., Zhang L., Li S.Z., Learning multi-scale block local binary patterns for face recognition, in: International Conference on Biometrics, Springer, 2007, pp. 828–837.
[49]
L. Wolf, T. Hassner, Y. Taigman, Descriptor based methods in the wild, in: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition, 2008.
[50]
Svab M., Computer-vision-based Tree Trunk Recognition, Fakulteta za računalništvo in informatiko, Univerza v Ljubljani, Slovenia, 2014.
[51]
Huang Z.-K., Huang D.-S., Du J.-X., Quan Z.-H., Guo S.-B., Bark classification based on gabor filter features using RBPNN neural network, in: International Conference on Neural Information Processing, Springer, 2006, pp. 80–87.
[52]
Huang Z.-k., Huang D.-s., Quan Z.-h., Bark classification using RBPNN based on Gabor filter in different color space, in: Information Acquisition, 2006 IEEE International Conference on, IEEE, 2006, pp. 946–950.
[53]
Chang C.-C., Lin C.-J., LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (3) (2011) 27.
[54]
Deng J., Dong W., Socher R., Li L.-J., Li K., Fei-Fei L., Imagenet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 248–255.
[55]
Liu L., Fieguth P., Zhao G., Pietikäinen M., Hu D., Extended local binary patterns for face recognition, Inform. Sci. 358 (2016) 56–72.
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Published: 01 March 2022
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