Image recognition method and image recognition device

Abstract

The invention discloses an image recognition method and an image recognition device. According to one concrete embodiment, the method comprises the following steps of: obtaining an image to be recognized containing an object to be recognized; sending the image to be recognized to a server, and receiving a confidence degree parameter and marker information of a target object corresponding to the object to be recognized, wherein the confidence degree parameter and the marker information are returned by a server and are obtained through recognition on the image to be recognized; when the confidence degree parameter is greater than a confidence threshold, using the marker information of the target object as a recognition result; when the confidence degree parameter is smaller than the confidence degree threshold, obtaining marking information associated with the image to be recognized from a third party platform; and using the marking information as a recognition result. The image recognition method and the image recognition device have the advantages that the effect that the server automatic recognition and the third party marking information are combined is achieved; the recognition accuracy is improved; the third part marking information is used for training a recognition model corresponding to a machine learning recognition mode used by the server; and the training effect of the recognition model is improved.

Description

technical field [0001] The present application relates to the field of computers, in particular to the field of image recognition, especially to an image recognition method and device. Background technique [0002] In daily life, users sometimes have a need to identify captured images. In the known technology, image recognition is to establish a recognition model by analyzing the features of a large number of images, and use the recognition model to recognize the image. However, when using the above method to recognize images, on the one hand, it is not suitable for individual users due to the large resource consumption in the recognition process; on the other hand, the recognition model can only be adjusted according to the recognition results output by the machine. When there are many errors in the output recognition results, it will cause deviations in the adjustment of the recognition model, which will lead to a decrease in the recognition accuracy. Contents of the in...

AI Technical Summary

Problems solved by technology

However, when using the above method to recognize images, on the one hand, it is not suitable for individual users due to the large resource consumption in the recognition process; on the other hand, the recognition model can only be adjusted according to the recognition results output by the machine. When there are many errors in the output recognition results, it will cause deviations in the adjustment of the recognition model, which will lead to a decrease in recognition accuracy

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