Open Access Open Access  Restricted Access Subscription or Fee Access

An Approach to Review on Automatic Image Annotation: A Survey

Hemlata Sahu, Hitesh Gupta


This paper provides an introduction to Automatic image annotation and also will discuss the literature survey on various approaches for automatic annotation on digital images. Which gives overview of Automatic image annotation is used to extract meaningful information with meaningful keywords and to develop significant relationships among variables stored in large data set. Automatic image annotation is the process of assigning keywords to digital images depending on the content information. Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. Many techniques have been proposed for image annotation in the last decade that gives reasonable performance on standard datasets. There are some researches on image annotation and produced very good knowledge theoretically or technically and lead to produce such promising surveys. However, most of these works fail for complex models and requires subsequent training. Summary and analysis of some of the approaches have been used as references to produce a framework in designing an automatic image annotation model.


Automatic Image Annotation, Annotation Approaches, Current Research, Image Retrieval and Image Search.

Full Text:



“A new roi grouping schema for automatic image annotation” by Rami albatal, Philippe mulhem, Yues chiaramella, 978-1-61284-350-6/11/$26.00 ©2011 IEEE

“Review on Statistical Approaches for Automatic Image Annotation” by Hua Wang, Heng Huang and Chris Ding , 2009 International Conference on Electrical Engineering and Informatics , 5-7 August 2009, Selangor, Malaysia

“Image Annotation Using Bi-Relational Graph of Images and Semantic Labels” by Hua Wang, Heng Huang and Chris Ding, pages 126–139, 2011.

“Technique of Image Retrieval based on Multi-label Image Annotation” by Ran Li, YaFei Zhang, Zining Lu, Jianjiang Lu, Yulong Tian-2010 Second International Conference on MultiMedia and Information Technology

“Ensemble Of Multiple Descriptors For Automatic Image Annotation” by Dongjian He & Yu Zheng, Shirui Pan, Jinglei Tang-2010 3rd International Congress on Image and Signal Processing

G. Tsoumakas, I. Katakis, and I. Vlahavas. Random k- Labelsets for Multi-Label Classification. TKDE, 2010.

“A Semantic Similarity Language Model to Improve Automatic Image annotation” Tianxia Gong, Shimiao Li, Chew Lim Tan School of Computing National University of Singapore, 1082-3409/10 $26.00 © 2010 IEEE

H. Muller, W. Muller, D. M. Square, S. M. Maillet and T. Pun, “Performance Evaluation in Content Based Image Retrieval: Overview and Proposals”, Pattern Recognition Letters, vol. 22, Apr. 2001.

J. Vogel and B. Schiele, “Performance Evaluation and Optimization for Content Based Image Retrieval”, Pattern Recognition Letters, vol. 22, Apr. 2001.

Y. Alp Aslandogan and Clement T. Yu, Senior Member, IEEE, Techniques and Systems for Image and Video Retrieval VOL. 11, NO. 1, JANUARY/FEBRUARY 1999

A. Hanbury, “A Survey of Methods for Image Annotation,” J. Vis. Lang. Comput., vol. 19, pp. 617-627, Oct. 2008.

J. Liu, B. Wang, M. Li, Z. Li, W. Y. Ma, H. Lu and S. Ma, “Dual Cross-Media Relevance Model for Image Annotation,” in Proceedings of the 15th International Conference on Multimedia 2007, p. 605 – 614.

C. F. Tsai and C. Hung, “Automatically Annotating Images with Keywords: A Review of Image Annotation Systems,” Recent Patents on Computer Science, vol 1, pp 55-68, Jan., 2008.

R. Datta, D. Joshi, J. Li and J. Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age,” ACM Computing Surveys (CSUR), vol 40, Apr. 2008.

Chung, K.P. and Fung, C.C. Relevance feedback and intelligent technologies in content-based image retrieval system for medical applications. Australian Journal of Intelligent Information Processing Systems, 8 (3). pp. 113-122, 2004.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.