基于统计特征聚类原理的图像识别技术

Statistic Features Clustering Analysis Based on Image Recognition

提出了基于模糊聚类原理的图像统计特征识别技术。通过分析象素灰度矩阵信息,提取出图像独立的统计特征量,建立起相应的图像统计特征模型空间Ω。由此,计算出各图像类的模糊相似系数矩阵,再运用聚类分析的传递闭包法将其改造成为模糊等价矩阵,划分出图像等价类,进而实现对目标图像的识别。实验结果表明,该方法能获得很好的图像识别效果。

Based on fuzzy cluster analysis, a scheme for recognizing image is presented. To analyze the spatial knowledge of pixels' hue matrix, and extract statistic features of image, a fuzzy similitude matrix is defined to describe the images' feature space 'Ω'. Introducing the fuzzy cluster analysis, a corresponding fuzzy equivalence matrix is obtained over the fuzzy similitude matrix by computing the transferred package. Under the biggest threshold 'λ', images are classified to two groups. The classification illustrates which group the unrecognized image belongs to, and what the unrecognized object is. A prototype system was developed to evaluate the effectiveness of the approach.The experiment shows that the proposed approach is effective on image recognition.