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Image classification method based on incoherence joint dictionary learning

A classification method and dictionary learning technology, which is applied in the field of image processing, can solve problems such as reducing the accuracy of image classification, and achieve the effects of optimizing image sparse representation, improving discrimination, and increasing accuracy

Active Publication Date: 2020-09-15
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, during the joint dictionary training process, shared features will appear in the class dictionary, which may reduce the accuracy of image classification

Method used

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  • Image classification method based on incoherence joint dictionary learning
  • Image classification method based on incoherence joint dictionary learning
  • Image classification method based on incoherence joint dictionary learning

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with specific examples, but the protection scope of the present invention is not limited thereto.

[0049] refer to Figure 1 to Figure 5 , an image classification method based on incoherent joint dictionary learning. Since different types of images contain shared features, the present invention trains a class dictionary for each type of image and a shared dictionary for all images. In addition, ensure the low rank of the shared dictionary to prevent the shared dictionary from absorbing the characteristics of the class dictionary itself, and add a coherent constraint item between the low-rank shared dictionary and the class dictionary to prevent the shared features from appearing in the class dictionary. The image classification method of dictionary learning, the model is as follows:

[0050]

[0051]

[0052]

[0053] Among them, the training samples Contains class C, Represents the c-th...

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Abstract

The invention discloses an image classification method based on incoherence joint dictionary learning. According to the method, a class dictionary is trained for each class of images, a shared dictionary is trained for all the images, the low rank of the shared dictionary is ensured to prevent the shared dictionary from absorbing class features, and coherence constraint items are added between thelow rank shared dictionary and the class dictionaries to prevent the shared features from appearing in the class dictionaries. According to the method, the discrimination of the training dictionary is improved, the sparse representation capability of the dictionary is improved, and the accuracy of image classification is further improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image classification method based on incoherent joint dictionary learning. Background technique [0002] In recent years, sparse representation has achieved great success in the field of image processing, such as image classification, image denoising, compressed sensing, etc., which represent signals as a linear combination of a few atoms in a redundant dictionary. In the process of sparse representation, the training dictionary largely determines the quality of sparse representation. [0003] At present, researchers have proposed a variety of dictionary training methods to improve the ability of sparse representation. The simplest dictionary training directly uses all samples as dictionaries, such as Sparse Representation Classification (SRC), but when the training samples are too large, the complexity of the algorithm is too high, and the training dicti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F16/55
CPCG06F16/55G06F18/28G06F18/241G06F18/214Y02T10/40
Inventor 李胜马悦何熊熊
Owner ZHEJIANG UNIV OF TECH