10 April 2018 Object recognition in images via a factor graph model
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061517 (2018) https://doi.org/10.1117/12.2303409
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.
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Yong He, Yong He, Long Wang, Long Wang, Zhaolin Wu, Zhaolin Wu, Haisu Zhang, Haisu Zhang, } "Object recognition in images via a factor graph model", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061517 (10 April 2018); doi: 10.1117/12.2303409; https://doi.org/10.1117/12.2303409
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