Abstract
With rapid advances in imaging devices and internet, millions of images are uploaded on the internet without much information about the image. An efficient method is necessary for detecting the concept of the desired image from this vast collection of images. In this paper, Support Vector Machine (SVM) based architecture is presented to detect concept of a given input image. To enhance the performance of proposed system, a bagging approach is implemented. Color moments, HSV Color Histogram, Grey level co-occurrence matrix, Wavelet Transform and Edge orientation histogram are used for image representation purpose. These low-level feature descriptors are used to train multiple SVM models. The final concept of the query image is obtained by voting from outputs of these multiple models. The proposed system is evaluated on Wang’s Corel 10K. Results of proposed system indicate its improved performance over existing systems.
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
Foschi, P.G., Kolippakkam, D., Liu, H., Mandvikar, A.: Feature extraction for image mining. In: Proceedings of International Workshop on Multimedia Information System, pp. 103–1099 (2002)
Tang, S., Zheng, Y.-T., Cao, G., Zhang, Y.D., Li, J.T.: Ensemble learning with LDA topic models for visual concept detection. In: Multimedia A Multidisciplinary Approach to Complex Issues, pp. 175–200 (2012)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-2440-0
Jiang, W., Zavesky, E., Chang, S.-F., Loui, A.: Cross-domain learning methods for high-level visual concept classification. In: ICIP, pp. 161–164 (2008)
Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10, 1055–1064 (1999)
Shi, R., Feng, H., Chua, T.-S., Lee, C.-H.: An adaptive image content representation and segmentation approach to automatic image annotation. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 545–554. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27814-6_64
Cusano, C., Ciocca, G., Schettin, R.: Image annotation using SVM. In: Proceedings of the Internet Imaging IV, vol. 5304. SPIE (2004)
Goh, K.S., Chang, E.Y., Li, B.: Using one-class and two-class SVMs for multiclass image annotation. IEEE Trans. Knowl. Data Eng. 17(10), 1333–1346 (2005)
Qi, X., Han, Y.: Incorporating multiple SVMs for automatic image annotation. Pattern Recognit. 40(2), 728–741 (2007)
Thai, H., Hai, T.S., Thuy, N.T.: Image classification using support vector machine and artificial neural network. Int. J. Inf. Technol. Comput. Sci. 4(5), 32–38 (2012)
Tian, D.: Support vector machine for automatic image annotation. Int. J. Hybrid Inf. Technol. 8(11), 435–446 (2015)
Laib, L., Ait-Aoudia, S.: Efficient approach for content based image retrieval using multiple SVM in CBIR. Comput. Sci. Inf. Technol. (2016)
Janwe, N.J., Bhoyar, K.K.: Semantic video concept detection using novel mixed-hybrid-fusion approach for multi-label data. Electron. Lett. Comput. Vis. Image Anal. 16(3), 14–29 (2017)
Smeulders, W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)
Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1075–1088 (2003)
Sangamnerkar, G.V., Bhoyar, K.K.: A neural network color classifier in HSV color space. In: International Conference on Industrial Automation and Computing (2014)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
Park, D.K., Jeon, Y.S., Won, C.S., Park, S.J.: Efficient use of local edge histogram descriptor. In: Proceedings of ACM International workshop on Standards, Interoperability and Practices, Marina del Rey, California, USA, pp. 52–54 (2000)
De Marsico, M., Riccio, D.: A new data normalization function for multibiometric contexts: a case study. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 1033–1040. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69812-8_103
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. BJU Int. 101(1), 1396–1400 (2008)
Ben-Hur, A., Weston, J.: A user’s guide to support vector machines. In: Carugo, O., Eisenhaber, F. (eds.) Data Mining Techniques for the Life Sciences. Humana Press, Totowa (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patil, S.M., Bhoyar, K.K. (2020). Improved Performance of Visual Concept Detection in Images Using Bagging Approach with Support Vector Machines. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_39
Download citation
DOI: https://doi.org/10.1007/978-981-15-4018-9_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4017-2
Online ISBN: 978-981-15-4018-9
eBook Packages: Computer ScienceComputer Science (R0)