1. A human-shape image segmentation method, characterized by comprising: step S1: extracting multi-scale context information for all first pixel points for training a human-shape image; step S2: sending image blocks of all scales of all the first pixel points into a same convolution neural network to form a multi-channel convolutional neural network group, wherein each channel corresponds to image blocks of one scale; step S3: training the neural network group using a back propagation algorithm to obtain human-shape image segmentation training model data; step S4: extracting multi-scale context information for all second pixels points for testing the human-shape image; step S5: sending image blocks of different scales of each of the second pixel points into a neural network channel corresponding to the human-shape image segmentation training model, wherein all of said neural network channels are merged together in a full-connected layer, a first value representing a first probability of said second pixel points belonging to a human-shape region is output at a first node of the last layer of the full-connected layer, and a second value representing a second probability of said second pixel points being outside of the human-shape region is output at a second node of the last layer of the full-connected layer; if said first probability is larger than said second probability, the second pixel points belong to the human-shape region, otherwise, the second pixel points are outside of the human-shape region.