A Review of Methods of Instance-based Automatic Image Annotation

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Author(s)

Morad Derakhshan 1,* Vafa Maihami 2

1. Graduate student of Software, Department of Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

2. Faculty member, Department of Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2016.12.04

Received: 1 Mar. 2016 / Revised: 27 Jun. 2016 / Accepted: 1 Sep. 2016 / Published: 8 Dec. 2016

Index Terms

Automatic Image Annotation, Instance-Based Nearest Neighbor, Semantic Gap, Voting Algorithm

Abstract

Today, to use automatic image annotation in order to fill the semantic gap between low level features of images and understanding their information in retrieving process has become popular. Since automatic image annotation is crucial in understanding digital images several methods have been proposed to automatically annotate an image. One of the most important of these methods is instance-based image annotation. As these methods are vastly used in this paper, the most important instance-based image annotation methods are analyzed. First of all the main parts of instance-based automatic image annotation are analyzed. Afterwards, the main methods of instance-based automatic image annotation are reviewed and compared based on various features. In the end the most important challenges and open-ended fields in instance-based image annotation are analyzed.

Cite This Paper

Morad Derakhshan, Vafa Maihami, "A Review of Methods of Instance-based Automatic Image Annotation", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.12, pp.26-36, 2016. DOI:10.5815/ijisa.2016.12.04

Reference

[1]A. Makadia, V. Pavlovic, and S. Kumar. 2010. Baselines for Image Annotation. International Journal of Computer Vision 90, 1 (2010), 88–105.
[2]M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. 2009. TagProp: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation. In Proc. of ICCV.
[3]J. Verbeek, M. Guillaumin, T. Mensink, and C. Schmid. 2010. Image annotation with TagProp on the MIRFLICKR set. In Proc. of ACM MIR.
[4]X. Li, C. Snoek, and M. Worring. 2009b. Learning Social Tag Relevance by Neighbor Voting. IEEE Transactions on Multimedia 11, 7 (2009), 1310–1322.
[5]D. Liu, X.-S. Hua, L. Yang, M. Wang, and H.-J.Zhang. 2009. Tag Ranking. In Proc. of WWW.
[6]G. Zhu, S. Yan, and Y. Ma. 2010. Image Tag Refinement Towards Low-Rank, Content-Tag Prior and Error Sparsity. In Proc. of ACM Multimedia.
[7]K. van de Sande, T. Gevers, and C. Snoek. 2010. Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 9 (2010), 1582–1596.
[8]J. Sang, C. Xu, and J. Liu. 2012. User-Aware Image Tag Refinement via Ternary Semantic Analysis. IEEE Transactions on Multimedia 14, 3 (2012), 883–895.
[9]L. Chen, D. Xu, I. Tsang, and J. Luo. 2012. Tag-Based Image Retrieval Improved by Augmented Features and Group-Based Refinement. IEEE Transactions on Multimedia 14, 4 (2012), 1057–1067.
[10]X. Li and C. Snoek. 2013. Classifying tag relevance with relevant positive and negative examples. In Proc. of ACM Multimedia.
[11]A. Znaidia, H. Le Borgne, and C. Hudelot. 2013. Tag Completion Based on Belief Theory and Neighbor Voting. In Proc. of ACM ICMR.
[12]Z. Lin, G. Ding, M. Hu, J. Wang, and X. Ye. 2013. Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions. In Proc. of CVPR.
[13]X. Zhu, W. Nejdl, and M. Georgescu. 2014. An Adaptive Teleportation Random Walk Model for Learning Social Tag Relevance. In Proc. of SIGIR.
[14]Y. Yang, Y. Gao, H. Zhang, J. Shao, and T.-S. Chua. 2014. Image Tagging with Social Assistance. In Proc. Of ACM ICMR..
[15]K. van de Sande, T. Gevers, and C. Snoek. 2010. Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 9 (2010), 1582–1596.
[16]G. Zhu, S. Yan, and Y. Ma. 2010. Image Tag Refinement Towards Low-Rank, Content-Tag Prior and Error Sparsity. In Proc. of ACM Multimedia.
[17]B. Truong, A. Sun, and S. Bhowmick. 2012. Content is still king: the effect of neighbor voting schemes on tag relevance for social image retrieval. In Proc. of ACM ICMR.