Abstract
Image classification is the elementary and widely addressed task in computer vision. Classification of monument architecture is a real challenge due to its structural complexity. Analyzing architecture using the machine learning or data mining techniques can be useful. Various feature descriptors are used for large scale image classification. In this context, optimal feature descriptors as filters are used in Convolutional Neural Network. When analyzing architecture using the machine learning or data mining techniques, it can be useful to infer whether it is Cathedral or Indian Mughal Monuments in architecture involved. This paper focuses on architecture recognition, which has been studied extensively in the history literature, using Convolutional Neural Network (CNN) based on highly effective Tensorflow, an open source software library. The important task is to generalize the model to perform well on the new database. Ontology performs a significance role in learning to categorize given picture into metaphysical classes. A deep learning based approach is used to train the convolutional neural network. In the proposed work, images are classified into Cathedrals and Indian Mughal Monuments. Experimental results demonstrate that the method can successfully classify if the given image is Cathedral or Indian Mughal Monument which mainly includes the Taj Mahal and Char Minar. Proposed method substantially outperform state-of art approaches which is demonstrated by satisfactory result obtained on the dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yuan, L., Qu, Z., Zhao, Y., Zhang, H., Nian, Q.: A convolutional neural network based on TensorFlow for face recognition. In: Vancouver, J. (ed.) Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd edn., vol. 2, pp. 68–73. Clarendon, Oxford (1892)
Fu, J., Mei, T., Yang, K., Lu, H., Rui, Y.: Tagging personal photos with transfer deep learning. In: Proceedings of the 24th International Conference on World Wide Web, pp. 344–354. International World Wide Web Conferences Steering Committee (2015)
Jia, S., Lansdall-Welfare, T., Cristianini, N.: Gender classification by deep learning on millions of weakly labelled images. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 462–467. IEEE (2016)
Gupta, U., Chaudhury, S.: Deep transfer learning with ontology for image classification. In: 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2015)
Gao, X.W., Hui, R.: A deep learning based approach to classification of CT brain images. In: SAI Computing Conference (SAI), pp. 28–31. IEEE (2016)
Goel, A., Juneja, M., Jawahar, C.V.: Are buildings only instances? Exploration in architectural style categories. In: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, p. 1. ACM (2012)
Bergado, J.R., Persello, C., Gevaert, C.: A deep learning approach to the classification of sub-decimetre resolution aerial images. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Harvard, pp. 1516–1519. IEEE (2016)
Meng, N., So, H.K.H., Lam, E.Y.: Computational single-cell classification using deep learning on bright-field and phase images. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 190–193. IEEE (2017)
Gerazov, B., Conceicao, R.C.: Deep learning for tumour classification in homogeneous breast tissue in medical microwave imaging. In: IEEE EUROCON 2017-17th International Conference on Smart Technologies, pp. 564–569. IEEE (2017)
Kuang, Z., Li, Z., Zhao, T., Fan, J.: Deep multi-task learning for large-scale image classification. In: 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), pp. 310–317. IEEE (2017)
Ji, S., Wei, X., Yang, M., Kai, Y.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)
Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1959)
Debnath, S., Soni, B., Baruah, U., Sah, D.K.: Text-dependent speaker verification system: a review. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), pp. 1–7. IEEE (2015)
Saini, A., Gupta, T., Kumar, R., Gupta, A.K., Panwar, M., Mittal, A.: Image based Indian monument recognition using convoluted neural networks. In: 2017 International Conference on Big Data, IoT and Data Science, pp. 138–142. IEEE (2017)
Das, T.K., Misra, S., Choudhury, S.P., Sah, D.K., Baruah, U., Laskar, R.H.: Comparison of DTW score and warping path for text dependent speaker verification system. In: 2015 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–4. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ninawe, A., Mallick, A.K., Yadav, V., Ahmad, H., Sah, D.K., Barna, C. (2021). Cathedral and Indian Mughal Monument Recognition Using Tensorflow. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1221. Springer, Cham. https://doi.org/10.1007/978-3-030-51992-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-51992-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51991-9
Online ISBN: 978-3-030-51992-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)