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Object identification method based on own coding

An object recognition and self-encoding technology, applied in the field of object recognition based on self-encoding, can solve problems such as unfavorable classifier classification applications, and achieve the effect of improving the classification effect and distinguishing features.

Active Publication Date: 2016-06-29
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

However, the feature extraction process of existing autoencoders is an unsupervised process, that is, there is no constraint between similar and heterogeneous sample points in the hidden layer mapping space, which is not conducive to the classification application of classifiers

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  • Object identification method based on own coding
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  • Object identification method based on own coding

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Embodiment Construction

[0037] The present invention proposes an object recognition method based on self-encoding. Firstly, the data of the existing labeled image database is used to train the self-encoder and the softmax classifier according to the steps, and the optimal classification function parameters are obtained as fixed parameters for identification; Then, during classification and recognition, the image data to be recognized is input into the trained self-encoder and classifier for classification and recognition. Considering that the existing autoencoder feature extraction process is an unsupervised process, that is, there is no constraint between similar and heterogeneous sample points in the hidden layer mapping space, so the present invention designs a supervised feature extraction process with Large-Margin regularization , so that the sample points in the hidden layer mapping space are close to each other and far away from each other, which can better facilitate the classification applica...

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Abstract

The invention relates to an object identification method based on own coding. In a training process, an own coder and a classifier are trained, and a Large-Margin regularization item is added to the training processor the own coder; and in an identification process, image data of an object to be identified is converted into a corresponding format and then input to the trained encoder and classifier to implement classified identification. Large-Margin monitored regularization is added to the own coder training process, so that sample points of the same class are clustered in a mapping space and sample points of different classes are far from one another, features of different classes are distinguished more obviously, and the classified identification effect is improved after that the characteristic data is input into the classifier.

Description

technical field [0001] The invention relates to the technical field of object recognition, in particular to an object recognition method based on self-encoding. Background technique [0002] Object recognition is one of the basic functions of machine intelligence, and it is the core problem and key technology in any practical application system that takes images or videos as input. Object recognition technology has a wide range of needs and applications in both military and civilian applications. [0003] In the prior art, the deep neural network has been widely used in the field of object recognition, and the autoencoder as its basic architecture is also being continuously improved and perfected. However, the feature extraction process of existing autoencoders is an unsupervised process, that is, there is no constraint between similar and heterogeneous sample points in the hidden layer mapping space, which is not conducive to the classification application of classifiers. ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/24
Inventor 刘伟锋马腾洲
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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