Abstract
Content-based image classification is being one of the important phase in the process of automatic retrieval and annotation of images. In this paper, we are focused on effective image classification using low-level colour and texture features. It is well proved that the classifier performances are good for the unknown images, if quite similar images are present in the training set. On the other hand, classifier performances could not be guaranteed for images that are very much dissimilar from training set. This generalization problem of classifier can bias the image retrieval and annotation process. This paper objective is to investigate the discrimination abilities for such different class standard images. For improved image classification, we extract the effective low-level colour and texture features from the images. These features include; local binary pattern (LBP) based texture features, and colour percentile, colour moment, and colour histogram based colour features. To overcome the generalization problem, we have used random forest classifier, capable for handle over-fitting situation. Experimental analysis on benchmark database confirms the effectiveness of this work.
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
Ng, W. W., Dorado, A., Yeung, D. S., Pedrycz, W., & Izquierdo, E.: Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error. Pattern Recognition 40(1), 19–32 (2007).
Vailaya, A., Figueiredo, M. A., Jain, A. K., & Zhang, H. J.: Image classification for content-based indexing. IEEE Transaction Image Processing 10 (1), 117–130 (2001).
Acharya, T., & Ray, A.: Image Processing Principles and Applications, John Wiley & Sons, (2005).
Jain, A. K., & Vailaya, A.: Image Retrieval Using Colour and Shape. Pattern recognition 29(8), 1233–1244 (1995).
Stricker, M. A., & Orengo, M.: Similarity of Colour Images. In: Proc. SPIE: Storage and Retrieval for Image and Video Databases 2420(3), 381–392 (1995).
Rao, A., Srihari, R. K., & Zhang, Z.: Spatial Colour Histograms for Content-Based Image Retrieval. In: 11-th IEEE International Conference on Tools with Artificial Intelligence pages, pp. 183–186 (1999).
Swain, M., & Ballard, D.: “Colour indexing” International Journal of Computer Vision 7(1), 11–32(1991).
Ojala, T., Pietikäinen, M., & Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29(1), 51–59 (1996).
Chang, T., & Kuo, C. J.: Texture analysis and classification with treestructured wavelet transform. IEEE Transactions on Image Processing 2(4), 429–441 (1993).
Turner, M.R.: Texture discrimination by Gabor functions. Biology, Cybernetics 55(4), 71–82(1986).
Singh, V. P., & Srivastava, R.: Design & performance analysis of content based image retrieval system based on image classification usingvarious feature sets. In: Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), pp. 664–670 (2015).
Yildizer, E., Balci, A. M., Hassan, M., & Alhajj, R.: Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble. Expert Systems with Applications 39(3), 2385–2396 (2012).
Zhang, Y. J.: Image Classification and Retrieval with Mining Technologies. In: Handbook of Research on Text and Web Mining Technologies, pp. 96–110 (2008).
Breiman, L.: Random forests. Machine learning 45(1), 5–32 (2001).
Bianconi, F., Harvey, R., Southam, P., & Fernández, A.: Theoretical and experimental comparison of different approaches for colour texture classification. Journal of Electronic Imaging20(4), 1–20(2011).
Wang, J., Li, J., & Wiederhold, G.: Simplicity: Semantics–sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, V.P., Srivastava, R. (2018). Improved Content-Based Image Classification Using a Random Forest Classifier. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_36
Download citation
DOI: https://doi.org/10.1007/978-981-10-3773-3_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3772-6
Online ISBN: 978-981-10-3773-3
eBook Packages: EngineeringEngineering (R0)