| The rapid development of computer and information technology makes theimage recognition technologies have been applied more and more widely. As the basis of the image processing, the image representation has been playing an increasingly vital role in computer vision and image recognition. In recent years, complex network theory isbecoming attractive for many scholars. This paper has focused on Image Modeling and Feature Extraction Method Based on Complex Netword and it makes some improvement.The main focus in this paper can be summarized two aspects as follows:1.Combining the Complex Network theory and Contour Recognition method together. This work extracts the target identification parameters by the topology information of the contour with Complex Network theory. In this way, the method has the advantages of the Complex Network theory and the Contour Recognition method.2.In order to describe the image structure more accurately, the direction information is considered in the undirected graph. Therefore, a directed complex network representation model is proposed in this paper. Firstly, we extract key points for an image and construct an initial complex network in which nodes correspond to the key points. Then, a novel dynamic evolution method called K-Nearest Neighbor evolution for the initial complex network model is devised to form a chain of directed sub-networks. At last,we propose a characteristics descriptor by extracting the features of the different stages to achieve image feature extracting and recognition.3.The traditional histogram method lost the spatial position information of thepixel points. Therefore, a method based on complex network nodes attribute(greyvalue) evolution is proposed in this paper. the image structural features extraction method based on the vertices weighted complex network model. |
