Image feature extraction method based on Laplace operator

Abstract

The invention discloses an image feature extraction method based on Laplace operator. The method in the invention introduces Laplace regularization least square factor in the process of feature extraction, using its characteristics to take into consideration potential manifold structure in data and distinctiveness of data. Meanwhile, in the method, to extract features that minimize covariance matrix of a result set, two different measuring methods can be used--trace optimization (A-optimality) of covariance matrix and determinant optimization (D-optimality) of covariance matrix, and two different algorithms are produced correspondingly, i.e., Laplace regularization A-optimal feature selection (LapAOFS) and Laplace regularization D-optimal feature selection (LapDOFS), respectively. The advantages of the method are that distinctive structure and geometric structure are considered at the same time, a proper feature subset can be extracted, and performance of subsequent learning process can be improved to the greatest extent while potential manifold structure of data is maintained.

Description

technical field [0001] The invention relates to the fields of feature selection, matrix dimension reduction, manifold and the like, in particular to an image feature extraction method based on a Laplacian operator. Background technique [0002] In many computer vision, pattern recognition, and data mining practices, objects such as images and text are often represented as points in high-dimensional Euclidean space. However, excessively high dimensionality significantly increases the time and space required for information processing. More importantly, basic learning tasks such as classification, aggregation, retrieval, etc. that are simple and feasible in low-dimensional spaces will be quite difficult in high-dimensional spaces with hundreds or thousands of dimensions. In order to solve this problem, feature selection and extraction technology selects meaningful feature subsets or feature combinations from the set of all features, and also reduces the dimensionality of feat...

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Problems solved by technology

However, in practical applications, we often face data without class labels, and the cost of adding class labels is quite high

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