single-jc.php

JACIII Vol.11 No.6 pp. 633-640
doi: 10.20965/jaciii.2007.p0633
(2007)

Paper:

Context Dependent Automatic Textile Image Annotation Using Networked Knowledge

Yosuke Furukawa, Yusuke Kamoi, Tatsuya Sato,
and Tomohiro Takagi

Human-Interface Laboratory, Computer Science Course, Meiji University, 1-1-1 Higashi-mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan

Received:
January 15, 2007
Accepted:
March 19, 2007
Published:
July 20, 2007
Keywords:
automatic image annotation, pattern recognition, ontologies
Abstract
This paper presents a new method of an automatic image annotation system that estimates keywords from an image. Typical automatic image annotation systems extract features from an image and recognize keywords. However this method has two problems. One is that it treats features statically. Features should change depending on what keywords are attached so keywords should not be treated equally. Another is that it does not consider the level of keywords. Visual keywords, such as color or texture, can be recognized easily from image features, while high-level semantics such as context are hard to recognize from the features. To solve these problems, our approach is to recognize context by using networked specialist knowledge and to recognize keywords by changing feature values dynamically depending on the context. To evaluate our system, we conducted two experiments of applying it to textile images. As a result, we obtained improved accuracy and confirmed the effectiveness of using networked knowledge.
Cite this article as:
Y. Furukawa, Y. Kamoi, T. Sato, and T. Takagi, “Context Dependent Automatic Textile Image Annotation Using Networked Knowledge,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.6, pp. 633-640, 2007.
Data files:
References
  1. [1] L. Bergman and V. Castelli, “Image Databases: Search and Retrieval of Digital Imagery,” Wiley-Interscience, 2001.
  2. [2] D. Blei and M. Jordan, “Modeling Annotated Data,” ACM SIGIR, 2003.
  3. [3] V. Cross and Y. Wang, “Semantic Relatedness Measures in Ontologies Using Information Content and Fuzzy Set Theory,” The 2005 IEEE International Conference on Fuzzy Systems, pp. 114-119, 2005.
  4. [4] C. Cusano, G. Ciocca, and R. Scettini, “Image Annotation Using SVM,” Proceedings of Internet Imaging IV, 2004.
  5. [5] P. Duygulu, K. Barnard, J. Freitas, and D. Forsyth, “Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary,” Proceedings of the European Conference on Computer Vision, 2002.
  6. [6] J. Fan, Y. Gao, H. Luo, and G. Xu, “Automatic Image Annotation by Using Concept-Sensitive Salient Objects for Image Content Representation,” Proceedings of the 27th annual international conference on Research and development in information retrieval, 2004.
  7. [7] J. Fan, Y. Gao, H. Luo, and G. Xu, “Statistical modeling and conceptualization of natural images,” The Journal of Pattern Recognition Society, 2005.
  8. [8] M. Gauld, C. Thies, B. Fischer, and T. Lehmann, “Combining global features for content-based retrieval of medical images,” Cross Language Evaluation Forum 2005, 2005.
  9. [9] J. Hare, P. Lewis, P. Enser, and C. Sandom, “Mind the Gap: Another look at the problem of the semantic gap in image retrieval,” Multimedia Content Analysis, Management, and Retrieval, 2006.
  10. [10] J. Hare, P. Sinclair, P. Lewis, K. Martinez, P. Enser, and C. Sandom, “Bridging the Semanic Gap In Multimedia Information Retrieval Top-down and Bottom-up Approaches,” In Proceedings of Mastering the Gap: From Information Extraction to Semantic Representation / 3rd European Semantic Web Conference, 2006.
  11. [11] J. Jeon, V. Lavrenko, and R. Manmatha, “Automatic Image Annotation and Retrieval using CrossMedia Relevance Models,” Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, 2003.
  12. [12] Y. Jin, L. Khan, L. Wang, and M. Awad, “Image Annotations By Combining Multiple Evidence & WordNet,” Proceedings of the 13th annual ACM international conference on Multimedia, 2005.
  13. [13] T. Kurita and T. Kato, “Sense Retrieval on an Image Database of Full Color Painting,” Information Processing Society of Japan, 1992.
  14. [14] D. Marr, “Vision: A Computational Investigation into the Human Representation and Processing of Visual Information,” W. H. Freeman and Company, 1982.
  15. [15] F. Monay and D. Perez, “On Image Auto-Annotation with Latent Space Models,” In Proc. ACM Inf. Multimedia, 2003.
  16. [16] F. Monay and D. Perez, “PLSA based Image Auto Annotation: Constraining the Latent Space,” In Proc. ACM Inf. Multimedia, 2004.
  17. [17] Y. Mori, H. Takahashi, and R. Oka, “Image-to-word transformation based on dividing and vector quantizing images with words,” Proceedings of the International Workshop on Multimedia Intelligent Storage and Retrieval Management, 1999.
  18. [18] H. Nakano, Y. Yoshida, and S. Yamamoto, “Signal Processing and Image Processing using Wavelet Transform,” Kyoritu Shuppan Company, 1999.
  19. [19] Y. Rubner, C. Tomasi, and L. J. Guibas, “A Metric for Distributions with Applications to Image Databases,” Proceedings of the 1998 IEEE International Conference on Computer Vision, 1998.
  20. [20] M. Suzuki, H. Kubota, and M. Tokutake, “Apparel sozai no kihon,” Senken Newspaper Company, 2004.
  21. [21] M. Tada and T. Kato, “Visual KANSEI Modeling based on Focal Area Analysis on Multiple Resolution and Hierarchical Classification,” Information Processing Society of Japan SIG-CVIM: Computer Vision and Image Media, 2004.
  22. [22] C. Ziegler and G. Lausen, “Spreading Activation Models for Trust Propagation,” IEEE International Conference on e-Technology, e-Commerce, and e-Service (EEE ’04), 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Dec. 06, 2024