Image classification method based on multi-sample dictionary learning and local constraint coding

Abstract

The invention discloses an image classification method based on multi-sample dictionary learning and local constraint coding, and belongs to the technical field of artificial intelligence and the field of image classification. The method comprises the following steps: firstly, generating a virtual training sample and an initialization dictionary for a training sample by utilizing a K-SVD algorithm; learning a dictionary and a coding coefficient of the training sample by using all the training samples and the initialized dictionary; and then learning the coding coefficient of the test sample byusing the learned dictionary, learning the linear classifier coefficient by using the coding coefficient of the training sample, and finally performing classification identification on the coding coefficient of the test sample by using the linear classifier coefficient and outputting a classification result. When the method is used for image classification and recognition, the learned dictionaryand coding coefficient matrix can have better representation capability, and the classification accuracy can be obviously improved.

Description

technical field [0001] The invention belongs to the technical fields of artificial intelligence and image classification, and in particular relates to an image classification method based on multi-sample dictionary learning and local constraint coding. Background technique [0002] With the rapid development of information science and technology, image data has exploded. In the face of these massive data, how to quickly and efficiently identify and classify management has become a problem to be solved. Image classification has a wide range of applications, including smart security, face recognition, search engines, etc. In practical applications, image classification has become a difficult and hot issue in the field of computer vision due to factors such as background complexity, illumination, and angles. [0003] In recent years, image classification using sparse coding algorithms has attracted much attention. Existing methods can be roughly divided into two categories: d...

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Problems solved by technology

[0006] From these methods, it can be concluded that learning a discriminative dictionary and obtaining a more sparse feature encoding can improve the accuracy of image classification. However, the above methods do not take these two aspects into consideration, resulting in poor results.

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