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Linear principal transformation: toward locating features in N-dimensional image space

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Abstract

Projection Functions have been widely used for facial feature extraction and optical/handwritten character recognition due to their simplicity and efficiency. Because these transformations are not one-to-one, they may result in mapping distinct points into one point, and consequently losing detailed information. Here, we solve this problem by defining an N-dimensional space to represent a single image. Then, we propose a one-to-one transformation in this new image space. The proposed method, which we referred to as Linear Principal Transformation (LPT), utilizes Eigen analysis to extract the vector with the highest Eigenvalue. Afterwards, extrema in this vector were analyzed to extract the features of interest. In order to evaluate the proposed method, we performed two sets of experiments on facial feature extraction and optical character recognition in three different data sets. The results show that the proposed algorithm outperforms the observed algorithms in the paper and achieves accuracy from 1.4 % up to 14 %, while it has a comparable time complexity and efficiency.

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Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

    Mohammad Mahdi Dehshibi

  2. Department of Computer Science, Shahid Beheshti University, GC, Tehran, Iran

    Mahmood Fazlali

  3. Department of Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Jamshid Shanbehzadeh

Authors
  1. Mohammad Mahdi Dehshibi
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  2. Mahmood Fazlali
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  3. Jamshid Shanbehzadeh
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Correspondence to Mohammad Mahdi Dehshibi.

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Dehshibi, M.M., Fazlali, M. & Shanbehzadeh, J. Linear principal transformation: toward locating features in N-dimensional image space. Multimed Tools Appl 72, 2249–2273 (2014). https://doi.org/10.1007/s11042-013-1505-x

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