基于纹理特征的图像复杂度研究

Abstract

In order to better describe the internal complexity of image,the establishment of the mathematic model between the image complexity and each index is the key step to study the complexity of image. Firstly,starting from the image texture,we try to establish a quantitative and precise mathematical description of the relationship between the image and the complexity of various indicators. There is no clear mathematical relationship between the image complexity and the measurable indicators,so gray level co-occurrence matrix( GLCM) is used to analyze the main characteristic parameters of the texture. The image complexity evaluationmethod is proposed based on BP neural network. Then a nonlinear mathematical evaluation model between image complexity and each index is established. And the weight values and index are obtained by the training for the neural network and learning through numbers of pictures. The verification results show that the evaluation model is able to reflect the internal complexity of the image truly,and the experimental results obtained are consistent with human visual perception. It is of a certain reference value for the application of BP neural network to study the image complexity.