一种基于小波矩的图像识别方法

A Method of Image Recognition Based on Wavelet Moment

研究了一种基于小波矩的图像目标平移、缩放和旋转不变特征提取算法,将不变特征提取算法与BP神经网络结合,组成一个图像识别系统.目的在于提高图像处理的质量,这种方法有更好的实用性.在这个系统中,利用小波矩不变量不仅可以得到图像的局部特征,还增加了对图像结构精细特征的把握能力强的优点,把提取的图像目标平移、缩放和旋转不变特征馈人BP神经网络,完成有监督的不变性模式识别.在实验中,利用该方法对无噪、有噪图像,特别是相似物体图像进行识别,可获得98％的正确识别率;并且将其与一般不变矩特征的算法获得的实验数据进行了对比分析.实验结果表明,该方法在图像识别准确率和抗噪性能上都有较大的提高.

A feature extraction approach of image object's invariant to the translation, scaling and rotation based on wavelet moment is studied. By combining the feature extracting algorithm with BP neural network, an image recognition system is established. Be aim at the quality of image processed is increased, this techniques has priority over other algorithm. In this system, by using wavelet moment invariant, not only the local feature of image object is obtained, but also the description ability for the fine features of image construct is improved. The invariant features extracted from image object are fed into BP neural network to perform the image recognition. In the experiment, using this method to recognize non-noise and noise-added images, especially similar images, the correct recognition rate of the method is up to 98%, besides. Test data attained from the algorithm based on general invariant moment is compared and analyzed. The experimental results demonstrate that the recognizing accuracy and anti-noise capability have been much increased.