Abstract
Image annotation is a procedure to interpret the semantic concepts associated with image objects and represent them as their textual descriptions. Automatic and manual techniques have been extensively discussed in recent years to annotate the image objects, but are not without limitations. Automatic image annotation techniques mainly consider a single classifier and a descriptor type to annotate the image objects. Furthermore, thesaurus based extensions and human-centered revisions of the annotations are usually not possible. The fine-tuning of classifiers is generally not supported. In contrast to this, manual image annotation improves the accuracy, but tedious to annotate huge collections of image objects. Alternatively, semi-automatic image annotation techniques are human-centered, enhances the efficiency, and also speed-up the annotation process by machine intervention. In this research, a semi-automatic image annotation framework is proposed to address limitations in automatic and manual image annotation techniques. Our image annotation framework considers multiple descriptors and artificial neural networks to annotate the image objects. Along with that, a voting mechanism is provided to recommend the suitable annotations extendible by thesaurus and human revisions. Revised and extended annotations employed further to fine-tune the classifiers. Image annotation framework is instantiated and tested on a real dataset by implementing an image annotation tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M.G. Van Doorn, A.P. de Vries, The psychology of multimedia databases, in Proceedings of the Fifth ACM Conference on Digital Libraries (ACM, New York, 2000), pp. 1–9
M. Kolodnytsky, N.O. Bernsen, L. Dybkjær, A visual interface for a multimodal interactivity annotation tool: design issues and implementation solutions, in Proceedings of the Working Conference on Advanced Visual Interfaces (ACM, New York, 2004), pp. 407–410
R. Baeza-Yates, B. Ribeiro-Neto, et al., Modern Information Retrieval, vol. 463 (ACM, New York, 1999)
A. Hanbury, A survey of methods for image annotation. J. Vis. Lang. Comput. 19(5), 617–627 (2008)
Y. Jin, L. Khan, L. Wang, M. Awad, Image annotations by combining multiple evidence & wordnet, in Proceedings of the 13th Annual ACM International Conference on Multimedia (ACM, New York, 2005), pp. 706–715
P.G. Enser, C.J. Sandom, P.H. Lewis, Automatic annotation of images from the practitioner perspective, in Proceedings of International Conference on Image and Video Retrieval (Springer, Cham, 2005), pp. 497–506
J. Li, J.Z. Wang, Real-time computerized annotation of pictures. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 985–1002 (2008)
P. Rogelj, S. Kovacic, Local similarity measures for multimodal image matching, in Proceedings of the First International Workshop on Image and Signal Processing and Analysis (IEEE, Piscataway, 2000), pp. 81–86
K. Zagoris, S.A. Chatzichristofis, N. Papamarkos, Y.S. Boutalis, Automatic image annotation and retrieval using the joint composite descriptor, in Proceedings of 14th Panhellenic Conference on Informatics (PCI) (IEEE, Piscataway, 2010), pp. 143–147
O.A. Penatti, E. Valle, R.d.S. Torres, Comparative study of global color and texture descriptors for web image retrieval. J. Vis. Commun. Image Represent. 23(2), 359–380 (2012)
A. Yavlinsky, E. Schofield, S.M. Rüger, Automated image annotation using global features and robust nonparametric density estimation, in Proceedings of International Conference on Image and Video Retrieval, vol. 3568 (Springer, Cham, 2005), pp. 507–517
Y. Zhao, Y. Zhao, Z. Zhu, J.-S. Pan, A novel image annotation scheme based on neural network, in Proceedings of Eighth International Conference on Intelligent Systems Design and Applications, vol. 3 (IEEE, Piscataway, 2008), pp. 644–647
L. Jiang, J. Hou, Z. Chen, D. Zhang, Automatic image annotation based on decision tree machine learning, in Proceedings of International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (IEEE, Piscataway, 2009), pp. 170–175
S. Rui, W. Jin, T.-S. Chua, A novel approach to auto image annotation based on pairwise constrained clustering and semi-naive bayesian model, in Proceedings of 11th International Conference on Multimedia Modeling (IEEE, Piscataway, 2005), pp. 322–327
M. Dong, C. Yang, F. Fotouhi, I2a: an interactive image annotation system, in Proceedings of IEEE International Conference on Multimedia and Expo (IEEE, Piscataway, 2005), pp. 1–4
B. Broda, H. Kwasnicka, M. Paradowski, M. Stanek, Magma—efficient method for image annotation in low dimensional feature space based on multivariate Gaussian models, in Proceeding of International Multiconference on Computer Science and Information Technology (IEEE, Piscataway, 2009), pp. 131–138
O.O. Karadag, F.T.Y. Vural, Hanolistic: a hierarchical automatic image annotation system using holistic approach, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (IEEE, Piscataway, 2009), pp. 16–21
P. Koletsis, E.G. Petrakis, Sia: semantic image annotation using ontologies and image content analysis, in Proceedings of International Conference Image Analysis and Recognition (Springer, Cham, 2010), pp. 374–383
A. Kerne, E. Koh, B. Dworaczyk, J.M. Mistrot, H. Choi, S.M. Smith, R. Graeber, D. Caruso, A. Webb, R. Hill, et al., Combinformation: a mixed-initiative system for representing collections as compositions of image and text surrogates, in Proceedings of the 6th ACM and IEEE-CS Joint Conference on Digital Libraries (ACM, New York, 2006), pp. 11–20
C. Halaschek-Wiener, J. Golbeck, A. Schain, M. Grove, B. Parsia, J. Hendler, Photostuff-an image annotation tool for the semantic web, in Proceedings of the 4th International Semantic Web Conference (2005)
A. Torralba, B.C. Russell, J. Yuen, Labelme: online image annotation and applications. Proc. IEEE 98(8), 1467–1484 (2010)
E. Skounakis, V. Sakkalis, K. Marias, K. Banitsas, N. Graf, Doctoreye: a multifunctional open platform for fast annotation and visualization of tumors in medical images, in Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society International Conference of the IEEE (IEEE, Piscataway, 2009), pp. 3759–3762
H. Peng, F. Long, E.W. Myers, Vano: a volume-object image annotation system. Bioinformatics 25(5), 695–697 (2009)
Acknowledgements
The authors would like to acknowledge the provision of the research facilities provided by Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan to carry out this research work. Authors would also acknowledge the finical support offered by Higher Education Commission, Pakistan to present this research work in the conference.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Rashid, U., Arif, B. (2018). PERIA-Framework: A Prediction Extension Revision Image Annotation Framework. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-77028-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-77028-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77027-7
Online ISBN: 978-3-319-77028-4
eBook Packages: EngineeringEngineering (R0)