Image feature extraction method based on Laplace operator

A Laplacian operator and image feature extraction technology, applied in the field of image feature extraction based on Laplacian operator, can solve the problem of high cost of data without class labels and class labels

Inactive Publication Date: 2012-09-12
ZHEJIANG UNIV
View PDF0 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practical applications, we often face data without

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image feature extraction method based on Laplace operator
  • Image feature extraction method based on Laplace operator
  • Image feature extraction method based on Laplace operator

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] With reference to accompanying drawing, further illustrate the present invention:

[0035] A kind of image feature extraction method based on Laplacian, this method comprises the following steps:

[0036] 1) Obtain original image feature data

[0037] Use the Internet to collect images, obtain an image library, and use a high-dimensional vector x for each image according to the visual characteristics of the image i =(f 1，i ,..., f n，i ) T said, f j，i Indicates the value of the j-th feature corresponding to the i-th image.

[0038] 2) Get the Laplacian matrix

[0039] All image feature vectors obtained in all steps 1) are expressed as a matrix X=(x 1 ,...,x m ), where each column vector x i All correspond to the vector obtained from step 1) of the i-th image, and each row vector corresponds to a certain feature. The goal of the method is to extract suitable features, that is, to extract suitable row vectors. use Represents the matrix represented by the last ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/46G06T7/00
Inventor 何晓飞卜佳俊陈纯刘晓
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap