Abstract
The research presented in the paper was inspired by the work of R. Douglas Fields. It transpired that not only neural structures in the brain play huge role in the process of understanding but also glial cells, which have so far been treated as passive cells with the task of protecting neuronal cells. This was a motivation to the proposed idea that currently extremely popular convolutional neural networks should be equipped with some elements corresponding to glial cells. In this work we present a modification of convolutional structures, which consist in adding additional adjustable parameters. The parameters control convolutional filter outputs. This approach allowed us to improve the quality of classification. In addition, the newly proposed structure is easier to interpret by indicating which filters are specific to a particular class of visual objects.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akusok, A., Miche, Y., Karhunen, J., Bjork, K.M., Nian, R., Lendasse, A.: Arbitrary category classification of websites based on image content. IEEE Comput. Intell. Mag. 10(2), 30–41 (2015)
Bas, E.: The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting. J. Artif. Intell. Soft Comput. Res. 6(1), 5–11 (2016)
Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Bezzi, P., Volterra, A.: A neuronglia signalling network in the active brain. Curr. Opin. Neurobiol. 11(3), 387–394 (2001)
Chang, T., Kuo, C.C.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)
Chu, J.L., Krzyzak, A.: The recognition of partially occluded objects with support vector machines and convolutional neural networks and deep belief networks. J. Artif. Intell. Soft Comput. Res. 4(1), 5–19 (2014)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)
Drozda, P., Grecki, P., Sopyla, K., Artiemjew, P.: Visual words sequence alignment for image classification. In: ICCI*CC, pp. 397–402. IEEE (2013)
Drozda, P., Sopyła, K., Górecki, P.: Online crowdsource system supporting ground truth datasets creation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 532–539. Springer, Heidelberg (2013)
Fields, R.D.: The Other Brain: From Dementia to Schizophrenia, How New Discoveries About the Brain are Revolutionizing Medicine and Science. Simon and Schuster, New York (2009)
Fields, R.D.: Neuroscience: map the other brain. Nature 501(7465), 25–27 (2013)
Francos, J., Meiri, A., Porat, B.: A unified texture model based on a 2-D Wold-like decomposition. IEEE Trans. Sig. Process. 41(8), 2665–2678 (1993)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv preprint arXiv:1207.0580
Huang, J., Kumar, S., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768, June 1997
Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195(1), 215–243 (1968)
Jagadish, H.V.: A retrieval technique for similar shapes. SIGMOD Rec. 20(2), 208–217 (1991)
Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304–3311. IEEE (2010)
Jégou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)
Kanimozhi, T., Latha, K.: An integrated approach to region based image retrieval using firefly algorithm and support vector machine. Neurocomputing 151, 1099–1111 (2015). Part 3(0)
Karakasis, E., Amanatiadis, A., Gasteratos, A., Chatzichristofis, S.: Image moment invariants as local features for content based image retrieval using the bag-of-visual-words model. Pattern Recogn. Lett. 55, 22–27 (2015)
Kiranyaz, S., Birinci, M., Gabbouj, M.: Perceptual color descriptor based on spatial distribution: a top-down approach. Image Vis. Comput. 28(8), 1309–1326 (2010)
Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)
Le Cun, B.B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems. Morgan Kaufman (1990)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)
Lin, C.H., Chen, H.Y., Wu, Y.S.: Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection. Expert Syst. Appl. 41(15), 6611–6621 (2014)
Liu, G.H., Yang, J.Y.: Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)
Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, WACV 1996, pp. 96–102, December 1996
Patgiri, C., Sarma, M., Sarma, K.K.: A class of neuro-computational methods for assamese fricative classification. J. Artif. Intell. Soft Comput. Res. 5(1), 59–70 (2015)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571, November 2011
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (ICLR 2014), p. 16. CBLS (2013)
Śmietański, J., Tadeusiewicz, R., Łuczyńska, E.: Texture analysis in perfusion images of prostate cancera case study. Int. J. Appl. Math. Comput. Sci. 20(1), 149–156 (2010)
Sopyła, K., Drozda, P., Górecki, P.: SVM with CUDA accelerated kernels for big sparse problems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 439–447. Springer, Heidelberg (2012)
Tabakow, P., Raisman, G., Fortuna, W., Czyz, M., Huber, J., Li, D., Szewczyk, P., Okurowski, S., Miedzybrodzki, R., Czapiga, B., et al.: Functional regeneration of supraspinal connections in a patient with transected spinal cord following transplantation of bulbar olfactory ensheathing cells with peripheral nerve bridging. Cell Transplant. 23(12), 1631–1655 (2014)
Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, pp. 87–119. Springer, London (2001)
Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)
Acknowledgements
This work was supported by the Polish National Science Centre (NCN) within project number DEC-2011/01/D/ST6/06957.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Korytkowski, M. (2016). A Novel Convolutional Neural Network with Glial Cells. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-39384-1_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39383-4
Online ISBN: 978-3-319-39384-1
eBook Packages: Computer ScienceComputer Science (R0)