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Image data multi-label classification method

A technology of image data and classification method, which is applied in the field of neural network, can solve the problem that the secondary correlation and multiple correlation of labels cannot be extracted at the same time, and achieve good prediction effect

Active Publication Date: 2019-09-06
ZHEJIANG UNIV
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Problems solved by technology

However, the current method cannot extract the secondary correlation and multiple correlation of tags at the same time.

Method used

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  • Image data multi-label classification method
  • Image data multi-label classification method

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with drawings and embodiments.

[0058] Embodiments of the present invention are as follows:

[0059] The label set of the specific implementation data set is {plant, bird, person, sky, cloud, grass, mountain}, figure 1 is a sample of this dataset. The input image x of this sample is the pixel point of the image. It can be seen that the label of the sample is {bird, sky, cloud}, and the label of the image is y=[0, 1, 0, 1, 1, 0, 0].

[0060] figure 2 A model structure of the invention is shown.

[0061] step one:

[0062] Obtain the image vector x=[x from the input image 1 , x 2 ,...,x p ] T Get the image feature set V emb ={v 1 x 1 , v 2 x 2 ,...,v p x p}, where x 1 , x 2 ,...,x p respectively represent the gray value of each pixel in the input image, x∈R p , R p Represents the p-dimensional real number space as the input space, p represents the total number of pixels in the inpu...

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Abstract

The invention discloses an image data multi-label classification method. The method comprises the following steps: decomposing an input image, extracting high-order correlation of features by utilizing a neural network, decomposing tag data, extracting high-order correlation of tags by utilizing the neural network, and decoding a feature code of the input image from an input space to a tag space by adopting the neural network comprising multiple layers of full connection layers; constructing a loss function, initializing a training parameter, adopting a random gradient descent method to minimize a final loss function as a target, and training and solving to obtain an optimal training parameter; and inputting to-be-tested image data into the trained model for prediction, and outputting to obtain a label result to realize multi-label classification. According to the method, the problem that the secondary correlation and the multi-correlation of the labels cannot be extracted at the sametime when a person works in front of the image data is solved, the prediction difficulty caused by too sparse image data is reduced, and the accuracy of multi-label classification is improved.

Description

technical field [0001] The invention relates to a neural network, a factorization machine and a multi-label classification method in the field of machine learning. Background technique [0002] With the rapid development of artificial intelligence, single-label classification is one of the most famous machine learning problems, where each instance is associated with a single label. However, in many real-world applications, an instance may be associated with multiple labels. For example, a piece of news might be about finance and politics, a video might be about government and policy, and an image might contain beaches and trees. Therefore, multi-label classification is an important research area. [0003] At present, people have done some research on multi-label classification and proposed many methods. The most critical challenge of multi-label classification is how to learn the correlation between labels. For this reason, it is proposed to extract one-time correlation,...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/241
Inventor 陈刚谌晨王皓波胡天磊陈珂寿黎但
Owner ZHEJIANG UNIV
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