参数优化的PCNN图像检索
Parameter Optimization in PCNN for Image Retrieval
作者: 郭 成 , 曾 亮 :国防科学技术大学计算机学院,湖南 长沙;
关键词: 脉冲耦合神经网络; 粒子群算法; 参数优化; 图像检索; Pulse Coupled Neural Network (PCNN); Particle Swarm Optimization (PSO); Parameter Optimization; Content-Based Image Retrieval (CBIR)
摘要:Abstract: When PCNN was used for image retrieval, the manual parameters selection became the difficulties and whether the selected parameters are good or not determines the retrieval results mostly. A novel method based on evolutionary learning for optimizing the parameters of Pulse Coupled Neural Network (PCNN) was proposed to overcome these problems. Firstly we classified some images of the database and trained them in advance by introducing the Particle Swarm Optimization (PSO) and restructured the fitness function to optimize the parameters which were used in image retrieval. Experimental results show that the proposed method can achieve the optimal parameters adaptively, and the retrieval results perform well even in the untrained images. The retrieval results convince that the proposed method was better than experienced parameters on precision ratio, recall ratio and personal visual judgment.
文章引用: 郭 成 , 曾 亮 (2015) 参数优化的PCNN图像检索。 软件工程与应用, 4, 115-120. doi: 10.12677/SEA.2015.46015
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