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A segment-wise prediction based on genetic algorithm for object recognition

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Abstract

Object recognition in complex backgrounds has challenged the fields of pattern recognition for years. It is even harder when the targets in images are of different poses. Current methods use descriptors of characteristic vectors and machine learning algorithms to produce classifiers for object recognition. However, the generalization ability of these methods relies on the quality of the training phase and cannot find the precise boundaries of the targets. The geometric features of objects are the most stable and consistent features, so the recognition method based on shapes can be more intuitive than those based on color and texture features. This paper proposes a novel method that uses the images represented by line segments. The recognition mission becomes to effectively filter and combine them. The contour fragments after combinations are expected to satisfy the given model, or certain parts of it. In this way, object recognition can be viewed as a combinatorial optimization problem. This paper develops a genetic algorithm-based method to solve this problem. The experimental results show that this method can solve the combinatorial optimization problem effectively and can accurately distinguish the contour of the target object from the background. This method, which is based on geometric features, may contribute to the development of explicit principals for the description of object structure and recognition method based on symbolic reasoning.

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Acknowledgements

This work was supported in part by the Key Project of the National Natural Science Foundation of China (No. 61134009), the National Natural Science Foundation of China (Nos. 61771146, 61473077, 61473078, 61503075), Shanghai Sailing Program (No. 17YF1426100), China Postdoctoral Science Foundation (No. 2016M601472).

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Authors and Affiliations

  1. Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University, Shanghai, China

    Xue-song Tang

  2. Laboratory of Cognitive Algorithm and Model, Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science, Fudan University, Shanghai, China

    Xue-song Tang & Hui Wei

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  1. Xue-song Tang
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  2. Hui Wei
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Correspondence to Hui Wei.

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Tang, Xs., Wei, H. A segment-wise prediction based on genetic algorithm for object recognition. Neural Comput & Applic 31, 2295–2309 (2019). https://doi.org/10.1007/s00521-017-3189-z

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