Automatic Image Annotation Using Semantic Text Analysis

  • Dongjin Choi
  • Pankoo Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7465)


This paper proposed a method to find annotations corresponding to given CNN news documents for detecting terrorism image or context information. Assigning keywords or annotation to image is one of the important tasks to let machine understand web data written by human. Many techniques have been suggested for automatic image annotation in the last few years. Many researches focused on the method to extract possible annotation using low-level image features. This was the basic and traditional approach but it has a limitation that it costs lots of time. To overcome this problem, we analyze images and theirs co-occurring text data to generate possible annotations. The text data in the news documents describe the core point of news stories according to the given images and titles. Because of this fact, this paper applied text data as a resource to assign image annotations using TF (Term Frequency) value and WUP values of WordNet. The proposed method shows that text analysis is another possible technique to annotate image automatically for detecting unintended web documents.


Image annotation Text analysis WUP measurement Semantic analysis 


  1. 1.
    Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised Learning of Semantic Classes for Image Annotation and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 394–410 (2007)CrossRefGoogle Scholar
  2. 2.
    Scheiber, A.T., Dubbeldam, B., Wielemaker, J., Wielinga, B.: Ontology-Based Photo Annotation. IEEE Intelligent Systems 16, 66–74 (2001)Google Scholar
  3. 3.
    Hollink, L., Schreiber, G., Wielemaker, J., Wielinga, B.: Semantic Annotation of Image Collections. In: Workshop on Knowledge Markup and Semantic Annotation, KCAP 2003 (2003)Google Scholar
  4. 4.
    Jeon, J., Lavrenko, V., Manmatha, R.: Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2003)Google Scholar
  5. 5.
    Feng, Y., Lapata, M.: Topic Models for Image Annotation and Text Illustration. In: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 831–839 (2010)Google Scholar
  6. 6.
    Tirilly, P., Claveau, V., Gros, P.: News image annotation on a large parallel text-image corpus. In: 7th Language Resources and Evaluation Conference, pp. 2564–2569 (2010)Google Scholar
  7. 7.
    David, M.B., Jordan, M.I.: Modeling annotated data. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 127–134 (2003)Google Scholar
  8. 8.
    Lavrenko, V., Manmatha, R., Jeon, J.: A Model for Learning the Semantics of Pictures. In: Advances in Neural Information Processing Systems 16 NIPS (2004)Google Scholar
  9. 9.
    Barnard, K., Johnson, M.: Word sense disambiguation with pictures. Journal Artificial Intelligence 167, 13–30 (2005)CrossRefGoogle Scholar
  10. 10.
    Deselaers, T., Ferrari, V.: Visual and Semantic Similarity in ImageNet. In: CVPR 2011 (2011)Google Scholar
  11. 11.
    Hwang, M., Choi, C., Kim, P.: Automatic Enrichment of Semantic Relation Network and Its Application to Word Sense Disambiguation. IEEE Transactions on Knowledge and Data Engineering 23(6), 845–858 (2011)CrossRefGoogle Scholar
  12. 12.
    Hwang, M., Choi, D., Choi, J., Kim, H., Koo, P.: Similarity Measure for Semantic Document Interconnections. An International Interdisciplinary Journal 13(2), 253–267 (2010)Google Scholar
  13. 13.
    Fern, S., Stevenson, M.: A Semantic Similarity Approach to Paraphrase Detection. In: Computer and Information Science (2008)Google Scholar
  14. 14.
    Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in PracticeGoogle Scholar
  15. 15.
    Choi, D., Kim, J., Kim, H., Hwang, M., Kim, P.: A Method for Enhancing Image Retrieval based on Annotation using Modified WUP Similarity in WordNet. In: 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (2012)Google Scholar
  16. 16.
    Hunker, J., Probst, C.W.: Insiders and Insider Threats-An Overview of Definitions and Mitigation Techniques. Journal of Wireless Mobile Networks, Ubiquitous Computing and Dependable Applications 2(1), 4–24 (2011)Google Scholar
  17. 17.
    Kiyomoto, S., Martin, K.M.: Model for a Common Notion of Privacy Leakage on Public Database. Journal of Wireless Mobile Networks, Ubiquitous Computing and Dependable Applications 2(1), 50–62 (2011)Google Scholar
  18. 18.
    Hwang, M., Choi, D., Kim, P.: A Method for Knowledge Base Enrichment using Wikipedia Document Information. An International Interdisciplinary Journal 13(5), 1599–1612 (2010)Google Scholar
  19. 19.
    Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: ACL 1994 Proceedings of the 32nd annual meeting on Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Dongjin Choi
    • 1
  • Pankoo Kim
    • 1
  1. 1.Dept. Of Computer EngineeringChosun UniversityGwangjuSouth Korea

Personalised recommendations