Method for identifying target based on local neighbor sparse representation

Abstract

The invention relates to a method for identifying a target based on local neighbor sparse representation. The method comprises the steps that: an input sample module outputs a c-class training sample set and a test sample set from a database; a sample unitization module unitizes the c-class training sample set and the test sample set to obtain a unitized training sample set A and a unitized test sample set Y; a local neighbor calculation module calculates the local neighbor of the test sample y in each class of the training set A for each test sample y in the unitized test sample set Y respectively; a linear reconfiguration weight vector calculation module reconfigures the test sample y by using each class of local neighbor linearity to obtain each class of linear reconfiguration weight vector, and simultaneously the linear reconfiguration weight vector needs to meet the normal number constraint requirement; a local neighbor sparse representation residual error calculation module calculates each class of local neighbor sparse representation residual error of the test sample y according to each class of the linear reconfiguration weight vector; and a classification module classifies the test sample y according to each class of the local neighbor sparse representation residual error.

Description

technical field [0001] The invention belongs to the technical field of computer-based pattern recognition, and specifically refers to a target recognition method based on local neighbor sparse representation. Background technique [0002] Most of the traditional signal representation theories are based on the transformation of non-redundant orthogonal basis functions, such as Fourier transform, Gabor transform, wavelet transform and so on. Their characteristic is that the representation form of a given signal is unique. For a given signal, once its characteristics do not completely match the basis functions, the resulting decomposition result will no longer be a sparse representation of the signal. Therefore, it is necessary to seek a new signal sparse representation method. In 1993, Mallat et al. first proposed a signal sparse representation method based on an over-complete Gabor dictionary, and proposed a matching pursuit (Matching Pursuit, MP) algorithm, thus creating a ...

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Problems solved by technology

[0009] Although the above method also emphasizes the discriminability suitable for classification, the whole process does not reflect the obvious discriminability. At the same time, there are certain restrictions on the experimental objects, and the time complexity is relatively high.

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