Discrimination-oriented correlated characters fusion recognition system of an image

Abstract

A discrimination-oriented correlated characters fusion recognition system of an image belongs to the technical field of telecommunication, comprising the following steps: first, determining a projection matrix via an iterative process according to a training image; second, respectively projecting the training image and test image to a characteristic space by utilizing the projection matrix, extracting the training characteristic and test characteristic and then carrying out parallel fusion respectively on the characteristics to form a new characteristic; at last, extracting characteristics with more discriminability by utilizing linear discriminant analysis method and adopting minimum distance classifier, thus identifying the category to which the test image belongs. The system can more naturally depict the relation between row vectors and the relation between column vectors of an image matrix; the extracted characteristics can more intensively reflect the image information; further discrimination-oriented processing can extract characteristics with more discriminability; and the system can improve not only processing speed but also recognition performance by being applied to the image recognition.

Description

technical field [0001] The invention relates to an image recognition system in the technical field of telecommunication, in particular to an identification-oriented image-related feature fusion recognition system. Background technique [0002] The feature fusion method based on Canonical Correlation Analysis (CCA) is to use CCA to establish the correlation criterion function between two sets of feature vectors, obtain the projection vector set, and then extract the combined related features. [0003] After searching the prior art documents, it is found that Q.S.Sun, S.G.Zeng, Y.Liu et al. published the article "Anew method of feature fusion and its application in image recognition" ("A new method of feature fusion and its application in image recognition"), it is proved that the features extracted by the standard correlation analysis method are not correlated, so they have good classification performance. However, this method is based on two sets of features in the form of ...

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Problems solved by technology

Moreover, CCA examines the correlation between two multidimensional variables, and extracts fewer related variables through projection transformation. These variables completely and simply describe the correlation between multidimensional variables and achieve the purpose of dimensionality reduction. However, the discriminative ability of these features may be insufficient, and the extracted features are not suitable as the basis for classifying and identifying targets.

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