International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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A New Hybrid Algorithm for Invariance and Improved Classification Performance in Image Recognition

  • Shi, Rui-Xia (Dept. of Energy Electrical Eng., Graduate School, Woosuk University) ;
  • Jeong, Dong-Gyu (Dept. of Energy Electrical Eng., Graduate School, Woosuk University)
  • Received : 2020.06.22
  • Accepted : 2020.07.03
  • Published : 2020.09.30
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Abstract

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

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References

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