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Image recognition method based on nonlinear enhanced subspace clustering

An image recognition and subspace technology, applied in character and pattern recognition, instruments, computing, etc., can solve problems such as affecting the accuracy of image recognition, lack of local information, etc., to improve the effect of image recognition and improve the effect of image recognition.

Active Publication Date: 2020-04-17
GUANGDONG UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0004] The present invention provides an image recognition method based on non-linear enhanced subspace clustering to solve the problem that the accuracy of image recognition is affected by the lack of local information in subspace clustering in existing image recognition methods

Method used

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  • Image recognition method based on nonlinear enhanced subspace clustering
  • Image recognition method based on nonlinear enhanced subspace clustering
  • Image recognition method based on nonlinear enhanced subspace clustering

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Embodiment 1

[0043] An image recognition method based on nonlinear augmented subspace clustering, such as figure 1 shown, including the following steps:

[0044] S1. Obtain an image data set; crop the images in the image data set to a uniform size; if the image data set contains a color image, perform dimensionality reduction processing on the color image;

[0045] The obtained image data set is denoted as X∈R (D*n) , D represents the dimension, and n is the total number of data points in the image data set;

[0046] S2. Solve the local linear expression matrix of the image data set by using a local linear embedding algorithm to extract the nonlinear manifold structure of the image data set:

[0047] Based on the KNN algorithm, according to the Euclidean distance as a measure, calculate the distance from the data point x in the image data set X i The nearest k nearest neighbors, if the j-th data point is the nearest neighbor of the i-th data point, then the i-th row and j-column of the ...

Embodiment 2

[0071] In this embodiment 2, a simulation experiment is carried out based on the image recognition method based on nonlinear enhanced subspace clustering provided in embodiment 1, which is specifically applied to face image recognition in this experiment. The experiment uses 5 data sets, including 3 face data sets: ORL, Yale Face, CMU-PIE data set; 2 object recognition data sets: COIL20, CIFAR-10 data set. The ORL contains 40 different subjects, and each object has 10 images taken in different situations, with different facial expressions, facial details and lighting conditions. The Yale Face dataset contains 165 images of 15 people. Each subject has 11 different images with different facial expressions and lighting conditions. CMU-PIE is a popular face dataset widely used in a variety of learning tasks. It includes 68 subjects, totaling 41,368 face images. The method in Example 1 is applied to object image clustering on COIL20 and CIFAR-10 respectively. The CIFAR-10 datas...

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Abstract

The invention discloses an image recognition method based on nonlinear enhanced subspace clustering. The method comprises the following steps: firstly, acquiring an image data set; solving a local linear expression matrix of the image data set by utilizing a local linear embedding algorithm so as to extract a nonlinear manifold structure of the image data set; constructing a nonlinear enhancementsubspace clustering objective function based on block diagonal constraint and a nonlinear manifold structure; initializing the nonlinear enhanced subspace clustering objective function and solving anoptimal solution; and constructing a Laplace matrix based on the optimal solution, and obtaining a clustering result of a final image data set through NCut or K-means to complete image recognition. According to the method, the nonlinear manifold structure of the image is learned in advance, that is, the nonlinear manifold is fitted by the local linear structure, so that the image recognition effect is improved; meanwhile, block diagonals are forcibly constructed to serve as constraint conditions, and the block diagonal structure of the adjacent matrix obtained through iterative solution betterconforms to the target effect of subspace clustering.

Description

technical field [0001] The invention relates to the technical field of pattern recognition computing, in particular to an image recognition method based on nonlinear enhanced subspace clustering. Background technique [0002] Face recognition is a very important perception ability of human beings. With the development of computer technology, face recognition has become an increasingly hot topic. The "swipe face payment" promoted by Alipay, social security management, subway "swipe face pass gate" and so on are all the embodiment of face recognition in real life applications. Since the face image will be affected by external factors such as illumination and posture and internal factors such as expression and age, in addition, some people's faces are very similar. These factors will increase the intra-class difference and inter-class similarity of face images, which will bring great difficulties to the recognition. The research proves that the face images under different il...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
CPCG06V40/172G06F18/23213
Inventor 陈少敏王丽娟尹明郝志峰蔡瑞初温雯陈炳丰
Owner GUANGDONG UNIV OF TECH
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