Research And Implementation Of The Broken-down Video’s Image Identification And Diagnosis Based On SVM

Digital image and video are applied ever widely in daily life. Meanwhile, people are expecting digital image and videos of higher accuracy. The videos’particularity made them susceptible to imaging systems, processing method, and a transmission medium of the recording apparatus, etc. Thus will inevitably apt to distortion or degradation phenomena. Therefore, identification and diagnose of digital image and video faults are needed, which could refer to the digital image quality assessment techniques.Using artificial recognition to detect image fault is undoubtedly very good, but now the digital image and video applications more widely, such as large-scale digital video monitoring system that integrates hundreds or even thousands of road camera images. Just simply rely on artificial fault identification of video image, the workload is enormous. Some of the existing video image fault detection system have ti go through a lot of experiments to determine an empirical threshold determination. This experience thresholds cannot meet the needs of different scenarios. When applied to a new scene, it also need a large number of experiments again to determine this threshold. Therefore, this paper introduces SVM machine learning methods, using objective evaluation algorithm to extract image visual feature, build up fault video images recognition model. It is not only ti deal with large-scale monitoring system, but also can break out different scenarios.This paper achieved identification and diagnosis of digital image and video fault based on SVM algorithms in machine learning, combining with digital image processing technology. The main aspects of the paper are as follows:(1) The paper exams machine learning theory, emphasizing introduction of SVM theory used in this paper, including Linear support vector machine (SVM)> Nonlinear support vector machine (SVM)、 Kernel function The steps the SVM learning algorithm.(2) The paper studies digital image quality evaluation method, and analyze evaluation function about the sharpness fault and color shift color specifically in the clarity, extracting characteristic vectors as input vector of SVM.(3) In-depth analyze SVM kernel and its parameters’impact on the classification model. In addition, the paper analyzes the optimal selection method of kernel function, kernel parameters, and SVM multi-type classification. Thus designed and implemented an image fault recognition model that can automatically identify the image failure. In order to improve the model’s recognition rate, the paper update the model by adding the negative sample to the training set. The image fault recognition model was applied to identify video image fault. Experiments has proved results using the model to identify the type of video fault accords with our observation.(4) Designed and developed a video image fault diagnosis system based on SVM.