Feature Extraction and Segmentation Processing of Images Based on Convolutional Neural Networks

Image segmentation can extract valuable information from images and has very important practical significance. In this paper, the application of Convolutional Neural Network (CNN) in image processing is studied. Full Convolutional Network (FCN) is used to improve the accuracy of image feature extraction and Visual Geometry Group-16 (VGG-16) is improved. In order to further improve the accuracy of image local positioning, the FCN output and the Conditional Random Field (CRF) are combined to obtain the FCN-CRF segmentation model and the model is analyzed based on the Weizmann Horse data set as the experimental object. The result suggests that the FCN-CRF model in this paper can achieve accurate segmentation of images with an average precision of 86.48& and the average intersection-over-union of 72.67%, which is significantly higher than Support Vector Machine (SVM), K-means and FCN algorithms. Moreover, it only takes around 0.3 s to process each image. The algorithm in this paper is proved to be reliable by the result. This research provides theoretical support for the application of CNN in image segmentation processing, which is conducive to the further development of image segmentation technology.