Sample image screening method and device, electronic device, and storage medium

Abstract

The invention discloses a sample image screening method and device, an electronic device and a storage medium, wherein, the method comprises the following steps: adopting an image classifier to predict each sample image to be screened respectively to obtain a feature vector and a classification label corresponding to each sample image; clustering feature vectors corresponding to each sample image,and respectively determining a clustering center to which each sample image belongs; merging the clustering centers according to the clustering centers and the classification tags corresponding to the sample images; selecting a target sample images according to the merging results. The disclosed sample image screening method can timely and accurately screen new data without manual intervention, can save human resources, and can further generate new labels through the screened new data.

Description

technical field

[0001] The present disclosure relates to the technical field of image processing, and in particular to a sample image screening method and device, electronic equipment, and a storage medium. Background technique

[0002] Recently, deep learning has been widely used in video image, speech recognition, natural language processing and other related fields. Convolutional neural network is an important branch of deep learning. Due to its strong fitting ability and end-to-end global optimization ability, the prediction accuracy of video image classification tasks is greatly improved after applying convolutional neural network. However, when an image is used for category prediction using a convolutional neural network, it can only give the probability that the image belongs to the label in the training data. For example, using a model trained on a data set that only includes "cat" and "dog", when predicting an image of "monkey", it can only give the probability tha...

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