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Combined method and system for extracting and classifying features of images

An image feature extraction and classifier technology, which is applied in the field of computer vision and image recognition, can solve the problems of not considering classification errors, low classification accuracy of feature extraction effectiveness, and inability to ensure that neighbors maintain features, etc.

Active Publication Date: 2016-05-25
苏州恒志汇智能科技有限公司
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AI Technical Summary

Problems solved by technology

But like LLE, NPE also uses the traditional Frobenius norm distance to measure the neighbor reconstruction error, so the common disadvantage of the two is that they cannot accurately measure the neighbor reconstruction error, and the Frobenius norm is very sensitive to noise
In addition, both are dimensionality reduction methods, which do not consider classification errors, that is, they cannot ensure that the extracted neighbor-preserving features are optimal for classification, and the effectiveness of feature extraction and classification accuracy are low.

Method used

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  • Combined method and system for extracting and classifying features of images
  • Combined method and system for extracting and classifying features of images
  • Combined method and system for extracting and classifying features of images

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Embodiment Construction

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0048] see figure 1 It shows a combined method of image feature extraction and classification disclosed by an embodiment of the present invention.

[0049] Depend on figure 1 It can be seen that the method includes:

[0050] S11: Obtain the neighbor samples of the training samples, construct the neighbor graph, and calculate the reconstruction coefficient matrix of the training samples.

[0051] Optionally, the method of the present invention adopts a K-nea...

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Abstract

The invention discloses a combined method and system for extracting and classifying features of images. The combined method comprises the following steps: firstly, constructing a neighbor graph according to the similarity of training samples and calculating and reconstructing a coefficient matrix; introducing nonlinear manifold learning with minimum neighbor reconstructing error measured on the basis of nuclear norm measurement, performing low-dimension manifold feature learning on a training image sample, thereby acquiring a linear projection matrix capable of extracting the low-dimension manifold feature of the sample; utilizing the low-dimension manifold feature of the training sample to minimize L2,1-norm regularization classifying error, completing robust sparse classifier learning and outputting the optimal classifier, thereby extracting and classifying the features of the tested sample. Compared with the prior art, the method has the advantage that the combination of nuclear norm measurement and L2,1-norm regularization is adopted for effectively increasing the descriptiveness of feature extraction and classification accuracy.

Description

technical field [0001] The present invention relates to the technical fields of computer vision and image recognition, and more specifically, relates to an image feature extraction and classification joint method and system. Background technique [0002] In a large number of practical applications, real data can be described by high-dimensional attributes or features. However, the dimension of the original feature may be very large, or the sample is in a very high-dimensional space, and the high-dimensional data can be transformed into a low-dimensional space through the method of feature mapping or feature transformation. Extracting the most effective features for classification from high-dimensional features has always been one of the very important and difficult research topics in the research fields of computer vision and image recognition. [0003] In order to extract better features, it is often necessary to consider the similarity or locality between data in the proc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24
Inventor 张召张妍李凡长张莉王邦军
Owner 苏州恒志汇智能科技有限公司
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