Abstract
Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts researchers to develop many algorithms for this application domain. The Multi-Instance Multi-Label Learning (MIML) is an important type of machine learning framework proposed recently for IMC. The drawbacks of the existing methods that they did not take into consideration are: (a) the description of the elementary characteristics from the image, (b) the correlation between labels. In this chapter, we propose a novel algorithm (MIML-GABORLPP), which handles these limitations. The algorithm uses Gabor filter bank as feature descriptor to handle the first limitation. It applies the Label Priority Power set as multi-label transformation to solve the problem of label correlation. The experimental work shows that the results of MIML-GABORLPP are better when compared to two other existing techniques.
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References
R.S. Cabral, F. Torre, J.P. Costeira and A. Bernardino, Matrix completion for multi-label image classification. In Advances in Neural Information Processing Systems, pp. 190–198, 2011.
S.J. Huang., W. Gao and Z.H. Zhou, “Fast multi-instance multi-label learning”. In Twenty-Eighth AAAI Conference on Artificial Intelligence, 21 June 2014.
Z. J. Zha, X. S. Hua, T. Mei, J. Wang,,G. J. Qi and Z. Wang, “Joint multi-label multi-instance learning for image classification”. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8). IEEE, June 2008.
ML. Zhang and ZH. Zhou, “A review on multi-label learning algorithms.” IEEE transactions on knowledge and data engineering 26.8: pp. 1819–1837, August 2014.
M.R. Boutell, J. Luo, X. Shen, and C.M. Brown, “Learning multi-label scene classification”. Pattern recognition, 37(9), pp. 1757–1771, 2004.
Z. Abdallah, A. El-Zaart, and M. Oueidat. “An Improvement of Label PowerSet Method Based on Priority Label Transformation.” International Journal of Applied Engineering Research 11.16: pp. 9079–9087, 2016.
M. Nosrati, R. Karimi, M. Hariri and K. Malekian, “Edge detection techniques in processing digital images: investigation of canny algorithm and gabor method”. World Applied Programming, pp. 116–21, March 2013.
S. Khan, M. Hussain, H. Aboalsamh, H. Mathkour, G. Bebis and M. Zakariah, “Optimized Gabor features for mass classification in mammography”. Applied Soft Computing, pp. 267–80, 31 July 2016.
N. Sonawane and BD. Phulpagar, “Review on Content-Aware Image Re-sizing Using Improved Seam Carving and Frequency Domain Analysis”, 2015
M. Kaur and U. Kaur, “Comparison between k-means and hierarchical algorithm using query redirection”. International Journal of Advanced Research in Computer Science and Software Engineering, July 2013.
Abdallah, Ziad, Ali El Zaart, and Mohamad Oueidat. “Experimental analysis and comparison of multilabel problem transformation methods for multimedia domain.” Applied Research in Computer Science and Engineering (ICAR), 2015 International Conference on. IEEE, 2015
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Abdallah, Z., El-Zaart, A., Oueidat, M. (2018). Proposed Multi-label Image Classification Method Based on Gabor Filter. In: Alja’am, J., El Saddik, A., Sadka, A. (eds) Recent Trends in Computer Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-89914-5_4
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DOI: https://doi.org/10.1007/978-3-319-89914-5_4
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