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Automated Generating Thai Stupa Image Descriptions with Grid Pattern and Decision Tree

  • Sathit PrasomphanEmail author
  • Panuwut nomrubporn
  • Pirat Pathanarat
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 652)

Abstract

This research presents a novel algorithm for generating descriptions of stupa image such as stupa’s era, stupa’s architecture and other description by using information inside image which divided into grid and learning stupa description from the generated information with decision tree. In this paper, we get information inside image by divided image into several grid patterns, for example 10 × 10 and use data inside that image to submit to the decision tree model. The proposed algorithm aims to generate the descriptions in each stupa image. Decision tree was used for being the classifier for generating the description. We have presented a new approach to feature extraction based on analysis of information in image by using the grid information. The algorithms were tested with stupa image dataset in Phra Nakhon Si Ayutta province, Sukhothai province and Bangkok. The experimental results show that the proposed framework can efficiently give the correct descriptions to the stupa image compared to using the traditional method.

Keywords

Automated generating image descriptions Decision tree Grid pattern Feature extraction 

Notes

Acknowledgment

This research was funded by King Mongkut’s University of Technology North Bangkok. Contract no. KMUTNB-59-GEN-048.

References

  1. 1.
    Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3128–3137 (2015)Google Scholar
  2. 2.
    Socher, R., Karpathy, A., Le, Q.V., Manning, C.D., Ng, A.Y.: Grounded compositional semantics for finding and describing images with sentences. TACL 2, 207–218 (2014)Google Scholar
  3. 3.
    Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
  4. 4.
    Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. TACL 2, 67–78 (2014)Google Scholar
  5. 5.
    Hodosh, M., Young, P., Hockenmaier, J.: Framing image description as a ranking task: data, models and evaluation metrics. J. Artif. Intell. Res. 47, 853–899 (2013)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Su, H., Wang, F., Yi, L., Guibas, L.J.: 3D-assisted image feature synthesis for novel views of an object. In: International Conference on Computer Vision (ICCV), Santiago (2015)Google Scholar
  7. 7.
    Temple Architecture: Available online at http://www.thailandbytrain.com/TempleGuide.html
  8. 8.
    Farhadi, A., Hejrati, M., Sadeghi, M.A., Young, P., Rashtchian, C., Hockenmaier, J., Forsyth, D.: Every picture tells a story: generating sentences from images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 15–29. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th International Conference on Computer Vision (ICCV 1999), Corfu, Greece (1999)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant key points. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Sathit Prasomphan
    • 1
    Email author
  • Panuwut nomrubporn
    • 1
  • Pirat Pathanarat
    • 1
  1. 1.Department of Computer and Information Science, Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkokThailand

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