关键词: 图像识别, 投影熵, 混合高斯模型, 最大期望算法, 判别分析

Abstract: A method based on projection entropy for image recognition was introduced in this paper. Since original definition of projection entropy does not make full use of image information and is not scale invariant, a new definition was proposed. The Local Projection Entropy (LPE) of normalized image was used for image recognition. In the process of recognition, firstly, Gaussian Mixture Model (GMM) of training set images’ LPE was obtained by Expectation Maximization (EM) algorithm. Then the Mahalanobis distance of target image’s LPE and GMM was calculated. The category of image was determined according to the distance discriminant law. Computer vision laboratory databases of Columbia university were used in the experiments, and the results show that the proposed algorithm is an effective approach for image recognition and has a proper structure for parallel computing.

Key words: image recognition, projection entropy, Gaussian Mixture Model (GMM), Expectation Maximization (EM) algorithm, discriminant analysis