An image semantic segmentation method based on local region conditional random field model

• Main Authors: YU YANZHEN, LI XUNGEN, ZHANG YUFAN, PAN MIAN
• Format: Patent
• Language: Chinese; English
• Published: 29.01.2019
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention relates to an image semantic segmentation method based on a local area conditional random field model. A full convolution neural network structure of the invention extracts input picturefeatures and obtains a rough segmentation result. The region selection structure filters the edge of the segmentation result map, and selects the segmentation result as the largest circumscribed rectangle of the pedestrian, bicycle and motor vehicle parts. The local region conditional random field model establishes the conditional random field model in the rectangular region and refines the segmentation result of the rectangular region. The invention effectively combines the advantages of the precision of the conditional random field model with the advantages of the speed of the full convolution neural network. The computational method of the conditional random field model is optimized so that the time complexity of the model is greatly reduced. The segmentation accuracy of the traditional full convolution neural