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Text-Transformed Image Classification Based on Data Compression

  • Nuo Zhang
  • Toshinori Watanabe
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)

Abstract

Image data analysis technology occupies an important position in processing multimedia information. Due to the wide usage of digital images, automatic classification technology with high capacity is necessary for processing enormous number of digital images. In this paper, we propose an automatic classification technique which representing text-transformed image based on data compression. Images are first transformed into texts, which are then divided into segments and replaced by characters. Then, instead of using texts themselves, the similarity between compressibility vectors of texts are used in the classification step, in which we focus on the compressibility of the text string. Finally, the effectivity of the proposed method is verified in our experiments.

Keywords

Segment Length Data Compression Nature Image Text Data Complex Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Richard, P.E.H., Duda, O., Stork, D.G.: Pattern classification, 2nd edn. John Wiley and Sons (2001)Google Scholar
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    Weiming Hu, Z.C.Z.F., Wu, O., Maybank, S.: Recognition of pornographic web pages by classifying texts and images. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1019–1034 (2007)CrossRefGoogle Scholar
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    Toshinori Watanabe, K.S., Sugihara, H.: A new pattern representation scheme using data compression. IEEE Trans. PAMI 24(5), 579–590 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Graduate School of Information SystemsThe University of Electro-CommunicationsTokyoJapan

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