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

Abstract

The present disclosure relates to a sample image screening method and device, electronic equipment, and a storage medium, wherein the method includes: using an image classifier to respectively predict each sample image to be screened, and obtain the corresponding feature vector and Classification labels; clustering the feature vectors corresponding to the sample images, respectively determining the cluster centers to which the sample images belong; Perform merging; filter out target sample images based on the merging results. Through the sample image screening method of the present disclosure, "new" data can be screened timely and accurately without manual intervention, which can save human resources, and "new" labels can be further generated 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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Problems solved by technology

Once the video of this kind of "new" data is bad or even illegal, it will cause immeasurable losses

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