高级检索

结合果蝇算法优化PCNN和相位一致性的图像检索

Image Retrieval Combining FOA Optimized PCNN and Phase Congruency

  • 摘要: 针对现有基于内容的图像检索方法不能有效地融合区域和边缘信息的不足,利用果蝇优化算法(FOA)优化脉冲耦合神经网络(PCNN),并将其与相位一致性(PC)相结合,提出一种图像检索方法.首先采用FOA对简化PCNN模型的3个关键参数进行设定,并结合最大信息熵准则进行图像分割和特征提取;然后利用PC提取图像的边缘,获取边缘颜色直方图特征;最后综合运用这2种特征进行图像检索.实验结果表明,该方法具有较好的检索性能,平均查准率和平均查全率均高于现有的代表性方法.

     

    Abstract: For resolving the problem that area and edge information can not be fused efficiently in content-based image retrieval, an image retrieval method combining optimized pulse coupled neural network(PCNN) and phase congruency(PC) is presented. Firstly, three key parameters of simplified PCNN model are set adopting fruit fly optimization algorithm(FOA), and image segmentation and feature extraction are performed based on the PCNN model and maximum information entropy criterion. Then image edge is extracted utilizing PC, and edge color histogram feature is obtained. Finally, image retrieval is achieved by integrating the two types of image features. Experimental results demonstrate that the proposed method has good retrieval performance, and its average precision and recall are higher than representative methods.

     

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