Image NMI Feature Extraction and Retrieval Method Based on Pulse Coupled Neural Networks

Abstract

In order to simply and effectively extract the information of important features in the image so as to improve the accuracy of the image retrieval, a novel algorithm of image normalized moment of inertia (NMI) feature extraction and retrieval based on pulse coupled neural networks (PCNN) is put forward. Firstly, the image is segmented into a series of binary correlation images using synchronous spatial-temporal characteristics of similar neurons and exponential attenuation mechanism of improved and simplified PCNN, and then a one-dimensional NMI feature vector signal of the binary series images, which can reflect the target shape and structure of the original image, is extracted, and applied to the image retrieval. Meanwhile, considering the correlation between binary series images and NMI sequence values differences between different images, the method of compounded similarity measurement of the combination of Mahalanobis distance and Pearson product-moment correlation is introduced. Experimental results show that the proposed algorithm has good performance of anti-geometric distortions and the uniqueness for different images expression to the vector sequence of image features, and has better image retrieval results.