Method and system for image classification based on quality embedding in the case of noisy labels

Abstract

The present invention provides an image classification method and system based on quality embedding in the case of noisy tags, including: a step of collecting network image tags; a tag quality factor embedding step: introducing a tag quality factor into a supervised image classification model for use in Control the prediction value generation of noisy labels and absorb the error return information from wrong labels; use the maximized logarithmic likelihood function to design the optimization objective function after adding the label quality factor; network model construction steps: use deep neural network to optimize Modeling of the objective function; network parameter training step: input training pictures and noisy labels into the network model, use a variant of stochastic gradient descent method to end-to-end linkage training model, and update model parameters at the same time; image classification step. The invention uniformly models the three variables of the real label of the picture, the label provided by the user and the quality of the picture label to form the supervised learning of the noisy label, and can obtain relatively accurate image classification results.

Description

technical field [0001] The present invention relates to the fields of computer vision and data mining, and in particular, relates to a method and system for learning a picture label when the label contains noise. Background technique [0002] Image recognition is a basic and important task in the field of artificial intelligence, and its application spans many fields such as natural science, medicine, and industry. With the rapid development of deep learning, image classifiers trained by convolutional neural networks have achieved unprecedented success. However, image classification learning under the deep learning framework relies on large-scale high-quality training data, including clear images and accurate labels. Such training data often comes from manual collection and labeling, which will consume a lot of manpower and material resources, making it relatively expensive and inefficient to deal with image recognition problems in new fields. [0003] Due to the rapid dev...

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Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide an image classification method and system based on quality embedding in the case of noisy labels, so as to solve the problem of using noisy label pictures to train image classifiers in the prior art. The quality of the image tag itself

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