Image Semantic Segmentation Approach for Studying Human Behavior on Image Data

Abstract

Image semantic segmentation is an essential technique for studying human behavior through image data. This paper proposes an image semantic segmentation method for human behavior research. Firstly, an end-to-end convolutional neural network architecture is proposed, which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network; then jump-connected convolution is used to classify each pixel in the image, and an image semantic segmentation method based on convolutional neural network is proposed; and then a conditional random field network is used to improve the effect of image segmentation of human behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field image is proposed. Finally, using the proposed image segmentation network, the input entrepreneurial image data is semantically segmented to obtain the contour features of the person; and the segmentation of the images in the medical field. The experimental results show that the image semantic segmentation method is effective. It is a new way to use image data to study human behavior and can be extended to other research areas.