Feature Reduction and Selection for Automatic Image Annotation

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 20)

Abstract

Automatic image annotation for large collections of images is a challenging problem. For labeling images precisely, more various features including low-level image features, EXIFs, textual tags of images are expected to be used. However, not all features contribute useful information for each concept. The high-dimension problem causing by combining all features is detrimental to the concept learning. In this paper we propose the feature reduction and selection method to improve the performance of annotating images. The proposed feature reduction methods extract informative features to reduce the dimensions. While the feature selection method based on the wrapper model can select effective features from miscellaneous features. The experimental result shows that the proposed feature reduction method improves the efficiency of concept learning. The developed feature selection method also increases the labeling precision and recall of images.

Keywords

Image Annotation Image Classification Feature Extraction Feature Selection Feature Reduction 

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References

  1. 1.
    Lin, Y.X., Chien, B.C.: Efficient Feature Reduction for High-Precision Text Classification. In: Proceedings of National Computer Symposium on Databases, Data Mining, and Information Retrieval, Chia-Yi, Taiwan (2011)Google Scholar
  2. 2.
    Kohavi, R., John, G.: Wrappers for Feature Subset Selection. Artificial Intelligence 97, 273–324 (1997)MATHCrossRefGoogle Scholar
  3. 3.
    Liu, W.Y., Dumais, S., Sun, Y., Zhang, H.J., Czerwinski, M., Field, B.: Semi-Automatic Image Annotation. In: Human-Computer Interaction, pp. 326–333 (2001)Google Scholar
  4. 4.
    Carneior, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learing of semantic class for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 394–410 (2007)CrossRefGoogle Scholar
  5. 5.
    Escalante, H.J., Hernández, C.A., Gonzalez, J.A., López-López, A., Montes, M., Morales, E.F., Sucar, L.E., Villaseñor, L., Grubinger, M.: The segmented and annotated IAPR TC-12 benchmark. Computer Vision and Image Understanding 114, 419–428 (2010)CrossRefGoogle Scholar
  6. 6.
    Jeon, J., Lavrenko, V., Manmatha, R.: Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. In: 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 119–126 (2003)Google Scholar
  7. 7.
    Wang, X.J., Zhang, L., Li, X., Ma, W.Y.: Annotation Images by Mining Image Search Result. IEEE Transactions of Pattern Analysis and Machine Intelligence 30(11), 1919–1932 (2008)CrossRefGoogle Scholar
  8. 8.
    Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: 14th International Conference on Machine Learning, pp. 412–420 (1997)Google Scholar
  9. 9.
    Chen, C., Liaw, A., Breiman, L.: Using random forest to learn imbalanced data. Techinical Report. no. 666, Department of Statistics, University of Berkely (2004)Google Scholar
  10. 10.
    Huiskes, M.J., Lew, M.S.: The MIR Flickr Retrieval Evaluation. In: ACM International Conference on Multimedia Information Retrieval (MIR 2008), Vancouver, Canada (2008)Google Scholar
  11. 11.
    Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image Indexing Using Color Correlograms. In: Conference on Computer Vision and Pattern Recognition, CVPR 1997, San Juan, Puerto Rico, pp. 762–768 (1997)Google Scholar
  12. 12.
    Chang, S.F., Sikora, T., Puri, A.: Overview of the mpeg-7 standard. IEEE Transactions on Circuits and Systems for Video Technology, 688–695 (2001)Google Scholar
  13. 13.
    Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Won, C.S., Park, D.K., Park, S.J.: Efficient Use of MPEG-7 Edge Histogram Descriptor. Electronics and Telecommunications Research Institute 24(1), 23–30 (2002)Google Scholar
  15. 15.
    Chatzichristofis, S.A., Boutalis, Y.S.: Fcth: Fuzzy color and texture histogram a low level feature for accurate image retrieval. In: 9th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2008, Klagenfurt, Austria, pp. 191–196 (2008)Google Scholar
  16. 16.
    Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)CrossRefGoogle Scholar
  17. 17.
    Wallace, G.K.: The JPEG Still Picture Compression Standard. Communications of the ACM - Special Issue on Digital Multimedia Systems 34, 30–44 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational University of TainanTainanTaiwan

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