Method and device for determining attribute of target in picture
A determination method and technology in the picture, applied in the computer field, can solve problems such as low efficiency and low efficiency of machine learning models, and achieve the effect of improving efficiency and saving manpower and material resources
Pending Publication Date: 2021-05-28
HANGZHOU HIKVISION DIGITAL TECH
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Problems solved by technology
[0003] Among them, supervised machine learning needs to label the training samples in advance to obtain labeled samples. When it is necessary to obtain high-precision machine learning models, supervised machine learning requires a large number of labeled samples, resulting in high-precision machine learning. The efficiency of the learning model is low, which in turn leads to the low efficiency of using the machine learning model to determine the attributes of the objects in the picture
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[0036] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.
[0037] In this application, "at least one" means one or more, and "multiple" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three types of relationships, for example, A and / or B, which can mean: A exists alone, A and B exist at the same time, and B exists alone, where A, B can be singula...
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The embodiment of the invention provides a method and device for determining the attribute of a target in a picture, and the method comprises the steps: obtaining labels of a plurality of test samples in a first test sample set through a first machine learning model, and adding the test samples of which the confidence coefficients of the labels are greater than a preset value and the labels of the test samples into a first labeled sample set, obtaining a second labeling sample set; wherein the first machine learning model is obtained by training based on the first labeled sample set; and obtaining a target machine learning model according to the second labeling sample set and a second machine learning model, with the target machine learning model being used for determining attributes of a target in the picture. According to the embodiment of the invention, a user does not need to label a large number of training samples, and the efficiency of determining the attributes of the target in the picture is improved.
Description
technical field [0001] The embodiments of the present application relate to computer technology, and in particular to a method and device for determining attributes of an object in a picture. Background technique [0002] Machine learning is a technology that can simulate or realize human learning behavior to acquire new knowledge or skills. The machine learning model can be divided into supervised machine learning and unsupervised machine learning in the training process. [0003] Among them, supervised machine learning needs to label the training samples in advance to obtain labeled samples. When it is necessary to obtain high-precision machine learning models, supervised machine learning requires a large number of labeled samples, resulting in high-precision machine learning. The efficiency of the learning model is low, which in turn leads to the low efficiency of using the machine learning model to determine the attributes of the objects in the picture. Contents of th...
Claims
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Login to View More IPC IPC(8): G06K9/62G06N3/04G06N3/08G06N20/00
CPCG06N3/08G06N20/00G06N3/045G06F18/40G06F18/241
Inventor 祝勇义
Owner HANGZHOU HIKVISION DIGITAL TECH