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Local extreme learning machine: local classification model for shape feature extraction

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Abstract

The shape feature of an object represents the geometrical information which plays an important role in the image understanding and image retrieval. How to get an excellent shape feature that has rotation, scaling and translation (RST) invariance is a problem in this field. This paper proposed a novel local extreme learning machine (LELM) classification algorithm to extract the shape features. LELM finds nearest neighbors of the testing set from the original training set and trains a local classification model. The shape feature is represented by an analytic decision function with a radial basis function (RBF) kernel obtained by LELM. Our method has the following advantages: (1) LELM not only keeps the local structure of the samples, but also solves the imbalance problem between variance and bias. (2) Features obtained by the RBF kernel are RST invariant. (3) LELM is more robust against the noise and fragmentation compared to other methods. We also demonstrate the performance of the proposed method in image retrieval.

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Acknowledgments

This research is supported by National Natural Science Foundation of China Nos. 61173163, 51105052, 61370200.

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Jing Zhang & Lin Feng

  2. School of Innovation Experiment, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Bin Wu

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  1. Jing Zhang
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  2. Lin Feng
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  3. Bin Wu
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Correspondence to Lin Feng.

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Zhang, J., Feng, L. & Wu, B. Local extreme learning machine: local classification model for shape feature extraction. Neural Comput & Applic 27, 2095–2105 (2016). https://doi.org/10.1007/s00521-015-2008-7

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  • DOI: https://doi.org/10.1007/s00521-015-2008-7

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