Application of SIFT Algorithm based on the Gabor Features in Multi-source Information Image Monitoring

Abstract

Under the background of today’s intelligence, the application of intelligent images is becoming popularized, and the detection of multi-source information images is a valuable research topic. Current image detection methods are insensitive and inaccurate. Therefore, the research combines the SIFT (scale-invariant feature transform) algorithm with the Gabor features and CNN (convolutional neural network (Ed note. Acronyms only need to be defined once.)) to form an improved new algorithm. The algorithm divides the features of the image into several different categories. In each category, the features will be fully identified and extracted, and different levels of feature matching will be performed. The properties of the SIFT algorithm are used to form an operational stacking pyramid and combine the Gabor features and CNN with it. The Gabor filter is formed into a filter bank to obtain parameters, including frequency, scale, and direction in various dimensions. The results are fused to obtain a fusion Gabor descriptor. The high sensitivity of CNN to images, particularly colors, is applied to the algorithm to make monitoring the algorithm more accurate. The experimental results show that the average precision rate, average recall rate, and average precision rate of the improved algorithm are 92.35%, 74.79%, and 82.55%, respectively, which are significantly higher than the other two algorithms used for comparison. The improved algorithm shows better performance and has remarkable advantages that can be applied to the image monitoring of image information.