Scene semantic segmentation method based on full convolution and long and short term memory units
A long-term and short-term memory and semantic segmentation technology, applied in the field of image semantic segmentation and deep learning, can solve the problems of over-segmentation of objects and low accuracy of scene image segmentation, and achieve the effect of solving low accuracy and improving accuracy.
Inactive Publication Date: 2017-12-15
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Abstract
The invention discloses a scene semantic segmentation method based on full convolution and a long-short term memory unit, relating to the technical field of image processing. The method includes the following steps of S1 constructing a deep neural network based on full convolution, a pyramid pooling module and long-short term memory unit module; S2 comparing a predictive image with a marked image, training by taking the Softmax loss as the objective function and the stochastic gradient descent as the optimization method, and updating the weight of the deep neural network obtained in step 1; S3 carrying out the S2 for many times, and completing the training until the loss is decreased to the limitation; and S4 inputting a new scene image to the trained deep neural network, and performing the bilinear interpolation to the original image resolution to obtain the semantic segmentation result of the scene. The method solves the problems that the current scene image segmentation is low in accuracy, and objects in the image are subjected to over-segmentation and under-segmentation.
Application Domain
Character and pattern recognitionNeural architectures
Technology Topic
Short durationImage segmentation +10
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