Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

A Survey on Multi-label Classification for Images

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 8
Year of Publication: 2017
Authors:
Radhika Devkar, Sankirti Shiravale
10.5120/ijca2017913398

Radhika Devkar and Sankirti Shiravale. A Survey on Multi-label Classification for Images. International Journal of Computer Applications 162(8):39-42, March 2017. BibTeX

@article{10.5120/ijca2017913398,
	author = {Radhika Devkar and Sankirti Shiravale},
	title = {A Survey on Multi-label Classification for Images},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {8},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {39-42},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume162/number8/27267-2017913398},
	doi = {10.5120/ijca2017913398},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The area of an image multi-label classification is increase continuously in last few years, in machine learning and computer vision. Multi-label classification has attracted significant attention from researchers and has been applied to an image annotation. In multi-label classification, each instance is assigned to multiple classes; it is a common problem in data analysis. In this paper, represent general survey on the research work is going on in the field of multi-label classification. Finally, paper is concluded towards challenges in multi-label classification for images for future research.

References

  1. Xuchun Li, Lei Wang, Eric Sung, “Multi-Label SVM Active Learning for Image Classification”, IEEE Image Processing ICIP '04, 2004.
  2. Yunchao Wei, Wei Xia, Min Lin, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, and Shuicheng Yan “HCP: A Flexible CNN Framework for Multi-label Image Classification”, IEEE Transactions On Pattern Analysis And Machine Intelligence, 2015.
  3. Tao Zeng and Shuiwang Ji, “Deep Convolutional Neural Networks for Multi-Instance Multi-Task Learning”, 2015 IEEE International Conference on Data Mining, Pages: 579-589, 2015.
  4. Sheng-Jun Huang and Zhi-Hua Zhou, “Multi-Label Learning by Exploiting Label Correlations Locally”, Proceedings of the 26th AAAI Conference on Artificial Intelligence, Pages: 949-955, 2012.
  5. Min-Ling Zhang and Zhi-Hua Zhou, “A Review on Multi-Label Learning Algorithms”, IEEE Transactions on Knowledge and Data Engineering, Volume: 26, No. 8, 2014.
  6. Meng Joo Er, Rajasekar Venkatesan, Ning Wang, “An Online Universal Classifier for Binary, Multi-class and Multi-label Classification” Cornell University Library, Pages: 1-6, 2016.
  7. G. Tsoumakas and I. Katakis, “Multi label Classification: An overview”, International Journal of Data Warehousing and Mining, Volume: 3, No. 3, Pages: 113, 2007.
  8. Meixiang Xu, Fuming Sun, Xiaojun Jiang, “Multi-label learning with co-training based on semi-supervised regression”, Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Pages: 175 - 180, 2014.
  9. Yong Luo, Dacheng Tao, Bo Geng, Chao Xu, Stephen J. Maybank, “Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification”, IEEE Transactions on Image Processing, Volume: 22, Issue: 2, Pages: 523 - 536, 2013.
  10. Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu, “CNN-RNN: A Unified Framework for Multi-label Image Classification”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Pages: 2285 - 2294, 2016.
  11. Ricardo Cabral, Fernando De la Torre, Joo Paulo Costeira, Alexandre Bernardino, “Matrix Completion for Weakly-Supervised Multi-Label Image Classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 37, Issue: 1, Pages: 121 - 135, 2015.
  12. Yan Huang, Wei Wang, Liang Wang, Tieniu Tan, “Multi-task deep neural network for multi-label learning”, 2013 IEEE International Conference on Image Processing, Pages: 2897 - 2900, 2013.
  13. Jesse Read, Antti Puurula, Albert Bifet, “Multi-label Classification with Meta-Labels”, 2014 IEEE International Conference on Data Mining, Pages: 941 - 946, 2014.
  14. Xiangyang Xue, Wei Zhang, Jie Zhang, Bin Wu, Jianping Fan, Yao Lu, “Correlative multi- label multi-instance image annotation”, 2011 International Conference on Computer Vision, Pages: 651 - 658, 2011.
  15. Teng Li, Shuicheng Yan, Tao Mei, Xian-Sheng Hua, In-So Kweon, “Image Decomposition With Multi-label Context: Algorithms and Applications”, IEEE Transactions on Image Processing, Volume: 20, Issue: 8, Pages: 2301 - 2314, 2011.
  16. Benhui Chen, Weifeng Gu, Jinglu Hu, “An improved multi-label Classification based on label ranking and delicate boundary SVM”, 2010 International Joint Conference on Neural Networks (IJCNN), Pages: 1 - 6, 2010.
  17. Yanwei Pang, Zhao Ma, Yuan Yuan, Xuelong Li, Kongqiao Wang, “Multimodal learning for multi-label image Classification”, 2011 18th IEEE International Conference on Image Processing, Pages: 1797 - 1800, 2011.
  18. Xinmiao Ding, Bing Li, Weihua Xiong, Wen Guo, Weiming Hu, Bo Wang, “Multi-Instance Multi-Label Learning Combining Hierarchical Context and its Application to Image Annotation”, IEEE Transactions on Multimedia, Volume: 18, Issue: 8, Pages: 1616 - 1627, 2016.
  19. Bo Wang, Zhuowen Tu, John K. Tsotsos, “Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification”, 2013 IEEE International Conference on Computer Vision, Pages: 425 - 432, 2013.
  20. Jian Wu, Victor S. Sheng, Jing Zhang, Pengpeng Zhao, Zhiming Cui “Multi-label Active Learning for Image Classification”, 2014 IEEE International Conference on Image Processing (ICIP), Pages: 5227 - 5231, 2014.
  21. Xin Li and Yuhong Guo, “Active Learning with Multi-Label SVM Classification”, IJCAI, Pages: 1479-1485, 2013.
  22. Aiwen Jiang, Chunheng Wang, Yuanping Zhu, “Calibrated Rank-SVM for Multi-Label Image Categorization”, IEEE, Pages: 1450-1456, 2008.
  23. Gulisong Nasierding and Atul Sajjanhar,“Multi-label Classification with Clustering for Image and Text Categorization”, 2013 6th International Congress on Image and Signal Processing IEEE, Pages: 869-874, 2013.
  24. Xin Li, Feipeng Zhao and Yuhong Guo, “Multi-label Image Classification with A Probabilistic Label Enhancement Model”, UAI’14, Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, Pages 430-439, 2014.
  25. Chen Ye, Jian Wu, Victor S. Sheng, Pengpeng Zhao, and Zhiming Cui, “Multi-Label Active Learning with Label Correlation for Image Classification”, ICIP 2015 IEEE, Pages: 3437-3441.
  26. Jiwei Hu, Kin Man Lam, Guoping Qiu, “A HIERARCHICAL ALGORITHM FOR IMAGE MULTI-LABELING”, IEEE 17th International Conference on Image Processing, Pages: 2349-2352, 2010.
  27. Wail Mustafa, Hanchen Xiong, Dirk Kraft, Sandor Szedmak, Justus Piater and Norbert Kruger, “Multi-Label Object Categorization Using Histograms of Global Relations”, IEEE 2015 International Conference on 3D Vision, Pages: 309-317, 2015.
  28. Xiaoyu Zhang, Jian Cheng, Changsheng Xu, Hanqing Lu, and Songde Ma, “MULTI-VIEW MULTI-LABEL ACTIVE LEARNING FOR IMAGE CLASSIFICATION”, ICME 2009 IEEE, Pages: 258-261, 2009.
  29. Gangadhara Rao Kommu, M.Trupthi and Suresh Pabboju, “A Novel Approach for Multi-label Classification using Probabilistic Classifiers”, IEEE International Conference on Advances in Engineering & Technology Research, 2014.
  30. Eva Gibaja, Manuel Victoriano, Jose Luis Avila-Jimenez, and Sebastian Ventura, “A TDIDT Technique for Multi-label Classification”, IEEE 2010, 519-524, 2010.
  31. Raed Alazaidah and Farzana Kabir Ahmad, “Trending Challenges in Multi Label Classification”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, Pages: 127-131, 2016.

Keywords

Multi-label Classification, Image annotation, machine learning, computer vision