Abstract
Revealing the context of a scene from low-level features representation, is a challenging task for quite a long time. The classification of landscapes scenes to urban and rural categories is a preliminary task for landscapes scenes understanding. Having a global idea about the scene context (rural or urban) before investigating its details, would be an interesting way to predict the content of that scene. In this paper, we propose a novel features representation based on skyline, colour and texture, transformed by a sparse coding using Stacked Auto-Encoder. To evaluate our proposed approach; we construct a new database called SKYLINEScene Database containing 2000 images of rural and urban landscapes with a high degree of diversity. Many experiments were carried out using this database. Our approach shows it robustness in landscapes scenes classification.
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
Alvarez, S., Vanrell, M.: Texton theory revisited: a bag-of-words approach to combine textons. Pattern Recognit. 45(12), 4312–4325 (2012)
Bergen, J.R., Julesz, B.: Rapid discrimination of visual patterns. IEEE Trans. Syst. Man Cybern. 13(5), 857–863 (1983)
Chen, Z., Chi, Z., Fu, H.: A hybrid holistic/semantic approach for scene classification. In: 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, 24–28 August 2014, pp. 2299–2304 (2014)
Lazzez, O., Ouarda, W., Alimi, A.M.: Age, gender, race and smile prediction based on social textual and visual data analyzing. In: Madureira, A.M., Abraham, A., Gamboa, D., Novais, P. (eds.) ISDA 2016. AISC, vol. 557, pp. 206–215. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53480-0_21
Lazzez, O., Ouarda, W., Alimi, A.M.: Understand me if you can! Global soft biometrics recognition from social visual data. In: Abraham, A., Haqiq, A., Alimi, A.M., Mezzour, G., Rokbani, N., Muda, A.K. (eds.) HIS 2016. AISC, vol. 552, pp. 527–538. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52941-7_52
Mokhtarian, F., Abbasi, S.: Shape similarity retrieval under affine transforms. Pattern Recognit. 35(1), 31–41 (2002)
Mokhtarian, F., Mackworth, A.K.: Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 34–43 (1986)
Mokhtarian, F., Suomela, R.: Robust image corner detection through curvature scale space. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1376–1381 (1998)
Nasri, H., Ouarda, W., Alimi, A.M.: ReLiDSS: Novel lie detection system from speech signal. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8 (2016)
Ouarda, W., Trichili, H., Alimi, A.M., Solaiman, B.: Towards a novel biometric system for smart riding club. J. Inf. Assur. Secur. 11(4) (2016)
Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Graph. Image Process. 1(3), 244–256 (1972)
Sassi, A., Ben Amar, C., Miguet, S.: Skyline-based approach for natural scene identification. In: 13th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2016, Agadir, Morocco, 29 November–2 December 2016, pp. 1–8 (2016)
Yao, J., Fidler, S., Urtasun R.: Describing the scene as a whole: joint object detection, scene classification and semantic segmentation. In: CVPR, pp. 702–709. IEEE Computer Society (2012)
Zhang, L., Verma, B., Stockwell, D.R.B., Chowdhury, S.: Spatially constrained location prior for scene parsing. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 1480–1486 (2016)
Acknowledgements
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
This work was funded by the “ANR-12-VBDU-0008 - Skyline” project of the “Agence Nationale de la Recherche (ANR)”, and by the LabEx “Intelligence des mondes Urbains - IMU”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sassi, A., Ouarda, W., Ben Amar, C., Miguet, S. (2019). Neural Approach for Context Scene Image Classification Based on Geometric, Texture and Color Information. In: Chen, L., Ben Amor, B., Ghorbel, F. (eds) Representations, Analysis and Recognition of Shape and Motion from Imaging Data. RFMI 2017. Communications in Computer and Information Science, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-030-19816-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-19816-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19815-2
Online ISBN: 978-3-030-19816-9
eBook Packages: Computer ScienceComputer Science (R0)