An improved patch matching algorithm based on fireworks algorithm

Huili Shi; Sheng Zhong; Luxin Yan

doi:10.1117/12.2538209

14 February 2020 An improved patch matching algorithm based on fireworks algorithm

Huili Shi, Sheng Zhong, Luxin Yan

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300W (2020) https://doi.org/10.1117/12.2538209
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

With the development of technology, especially the rapid development of hand-held devices, it is more convenient to obtain video sequences, but the video quality still suffers from some issues, such as unwanted camera shakes and jitter. To address the issues, video stabilization techniques have been developed to obtain high quality and stable videos. Considering computational complexity and real-time requirements, patch matching, has become an important method for motion estimation and video stabilization. It transforms the video stabilization task into a minimum optimization problem. In this paper, we propose a novel patch matching method integrated with fireworks algorithm[1] for motion search, which is a novel swarm intelligence optimization algorithm. Inspired by the fireworks explode in the air, the established mathematical model can be formulated as a parallel explosive search method by introducing random factors and selection strategies, and thus developed into a global probability search method for solving the optimal solution of complex optimization problems. It has excellent performance and high efficiency in solving complex optimization problems. Experimental results show that the improved patch matching method based on fireworks algorithm has achieved better results, compared with the ones with traditional motion search algorithms.

Citation Download Citation

Huili Shi, Sheng Zhong, and Luxin Yan "An improved patch matching algorithm based on fireworks algorithm", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300W (14 February 2020); https://doi.org/10.1117/12.2538209

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