Commenced in January 2007
Paper Count: 31830
An Improvement of Multi-Label Image Classification Method Based on Histogram of Oriented Gradient
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 the researchers to develop many algorithms for this application domain. The existing techniques for IMC have two drawbacks: The description of the elementary characteristics from the image and the correlation between labels are not taken into account. In this paper, we present an algorithm (MIML-HOGLPP), which simultaneously handles these limitations. The algorithm uses the histogram of gradients as feature descriptor. It applies the Label Priority Power-set as multi-label transformation to solve the problem of label correlation. The experiment shows that the results of MIML-HOGLPP are better in terms of some of the evaluation metrics comparing with the two existing techniques.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128801Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1130
 MZ Kurian, "Various Object Recognition Techniques for Computer Vision" Journal of Analysis and Computation 2011, vol. 7, pp. 39-47.
 Y. Ramadevi, T. Sridevi, B. Poornima, and B. Kalyani. "Segmentation and object recognition using edge detection techniques" International Journal of Computer Science & Information Technology (IJCSIT), vol. 2, 2010, pp. 153-161.
 T. Sikora. "The MPEG-7 visual standard for content description-an overview" IEEE Transactions on circuits and systems for video technology, vol. 11, 2001, pp. 696-702.
 Z.H. Zhou, Min-Ling Zhang, Sheng-Jun Huang, and Yu-Feng Li. "Multi-instance multi-label learning" Artificial Intelligence vol. 176,2012, pp. 2291-2320.
 Z. Abdallah, Ali El-Zaart, and Mohamad Oueidat, "An Improvement of Label PowerSet Method Based on Priority Label Transformation" International Journal of Applied Engineering Research, vol. 11, 2016, pp. 9079-9087
 I. Pillai, Giorgio Fumera, and Fabio Roli. "Designing multi-label classifiers that maximize F measures: State of the art" Pattern Recognition, vol. 61, 2017, pp. 394-404.
 N. SpolaôR, Everton AlvaresCherman, Maria Carolina Monard, and Huei Diana Lee. "A comparison of multi-label feature selection methods using the problem transformation approach" Electronic Notes in Theoretical Computer Science, vol. 292, 2013, pp. 135-151.
 M.R. Boutell, Luo, J., Shen, X. and Brown, C.M., "Learning multi-label scene classification" Pattern recognition, vol. 37, 2004, pp.1757-1771.
 G. Tsoumakas, K, Ioannis Katakis, and Ioannis Vlahavas. "Data mining and knowledge discovery handbook." Mining multi-label data 2010.
 M. Zhang and Z. Zhou, "A review on multi-label learning algorithms" Knowledge and Data Engineering, IEEE Transactions on, vol. InPress, 2013.
 J. MacQueen. "Some methods for classification and analysis of multivariate observations" In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, 1967, pp. 281-297.
 M. Hatto. "Acceleration of Pedestrian Detection System using Hardware-Software Co-design" 2015, pp 7.
 H. Al-Shamlan and A. El-Zaart. "Feature extraction values for breast cancer mammography images" Bioinformatics and Biomedical Technology (ICBBT), 2010 International Conference.
 A. Gumaei, A. El-Zaart, M. Hussien and M. Berbar, "Breast segmentation using k-means algorithm with a mixture of gamma distributions", Broadband Networks and Fast Internet (RELABIRA), 2012 Symposium.