Deep learning-based weakly supervised salient object detection method and system

Abstract

The invention discloses a deep learning-based weakly supervised salient object detection method and system. The method comprises the steps of generating salient images of all training images by utilizing an unsupervised saliency detection method; by taking the salient images and corresponding image-level type labels as noisy supervision information of initial iteration, training a multi-task fullconvolutional neural network, and after the training process is converged, generating a new type activation image and a salient object prediction image; adjusting the type activation image and the salient object prediction image by utilizing a conditional random field model; updating saliency labeling information for next iteration by utilizing a label updating policy; performing the training process by multi-time iteration until a stop condition is met; and performing general training on a data set comprising unknown types of images to obtain a final model. According to the detection method and system, noise information is automatically eliminated in an optimization process, and a good prediction effect can be achieved by only using image-level labeling information, so that a complex andlong-time pixel-level manual labeling process is avoided.

Description

technical field [0001] The present invention relates to the field of computer vision based on deep learning, in particular to a method and system for weakly supervised salient object detection based on deep learning. Background technique [0002] Salient object detection refers to accurately locating the regions in an image that most attract human visual attention. The fact that this technique can be used in many different vision techniques has stimulated a lot of research work in computer vision and cognitive science in recent years. [0003] In recent years, the successful application of convolutional neural networks has brought major breakthroughs to saliency detection technology, such as the research work "Visual saliency based on multiscale deep features" (IEEEConference on Computer Vision and Pattern Recognition (CVPR), June 2015), and the research work of N. Liu et al. in 2016 "Deep hierarchical saliency network for salient object detection" (In Proceedings of the IE...

AI Technical Summary

Problems solved by technology

These methods usually make predictions based on some low-level features, such as color, position, background prior information, etc., which leads to such methods that are always suitable for specific categories of images, but cannot make good predictions for all images. , these low-level feature-based methods have a common disadvantage, that is, most of the detection errors originate from the lack of consideration of spatial correlation and image semantics

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