A semantic segmentation method of weakly supervised image based on spatial pyramid concealment pooling

Abstract

The invention discloses a weak supervised image semantic segmentation method based on spatial pyramid concealment pooling, which comprises the following steps: selecting a convolution neural network H, processing the input image X through the convolution neural network H to obtain a classification characteristic map; the spatial pyramid pooling module is established according to the classificationcharacteristic map, and then the spatial pyramid is concealed to obtain the output characteristic map. According to the output characteristic graph, the category activation vector and the category probability vector are calculated, and then the competitive spatial pyramid masking pooling loss function is established. The convolutional neural network H is trained according to the pooling loss function of competitive spatial pyramid concealment and the segmentation feature map is extracted. The invention realizes a weak supervised image semantic segmentation model with richer local features, more perfect region feature mining and more robust target size and posture, improves the extraction ability of local semantic information and strengthens the recognition ability of local targets or parts in the weak supervised semantic segmentation.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for semantic segmentation of weakly supervised images based on spatial pyramid masking pooling. Background technique [0002] Image semantic segmentation is a basic computer vision task, and its goal is to classify all pixels in the image. Since it can fully understand images at the pixel level, it is useful for other vision tasks such as image classification and object recognition. However, because the production of pixel-level label data requires a lot of energy, fully supervised image semantic segmentation is difficult to quickly achieve large-scale expansion, so weakly supervised image semantic segmentation methods that rely on image category labels have been widely studied. [0003] In past computer vision research, the pyramid model has been used a lot. The well-known SIFT algorithm extracts key point description features through the Laplacian...

AI Technical Summary

Problems solved by technology

On the other hand, although the pyramid model has the characteristics of multi-scale information and local information fusion, the problem of how to better mine the sub-regional semantic information on the basis of mastering the global information has not been completely solved.

Images