Image classification method based on self-modulated dictionary learning

A technology of dictionary learning and classification methods, applied in character and pattern recognition, instruments, computer parts, etc.

Inactive Publication Date: 2013-05-22
NANJING UNIV
View PDF3 Cites 40 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although many researchers at home and abroad have devoted themselves to the research of ima

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 classification method based on self-modulated dictionary learning
  • Image classification method based on self-modulated dictionary learning
  • Image classification method based on self-modulated dictionary learning

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0067] Examples:

[0068] This embodiment is divided into a training phase and a classification phase. The main processes of each embodiment are described below:

[0069] Training process:

[0070] 1. Local feature extraction: local feature extraction is performed on a set of training images I. The local feature descriptor can effectively represent the local information of the image, which provides a basis for the formation of the subsequent overall image description. For tasks such as target recognition, the SIFT feature has a good effect, so this embodiment uses the SIFT feature as the local feature of the image. In addition, when extracting local features of an image, you also need to determine the sampling strategy, that is, dense sampling or sparse sampling (point of interest sampling). The two sampling methods are divided by the number of sampling points in an image. If only some interesting points of an image are sampled, and the number of sampling points is relatively small...

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 classification method based on self-modulated dictionary learning. The method includes the steps: in the training phase, step 1, local feature extracting,; performing local feature descriptor on every training image of a training image collection to extract and generate local feature collection of the training image collection; step 2, self-modulated dictionary learning; step 3, training image displaying which comprises two sub-steps of feature sparse coding and image space aggregation; step 4, classification model learning; step 5, local feature extracting; step 6, images to be classified displaying; step 7, model predicting. The image classification method introduces reasonable ordering mechanism to dictionary learning, designs a dictionary learning method of self-modulated mechanism, and combines image classification to analyze and verify to improve accuracy of image classification eventually.

Description

technical field [0001] The invention belongs to the field of image classification, in particular to a multi-category-oriented, high-precision image classification method. Background technique [0002] In the current information society, digital media resources represented by images have reached a massive scale and become the main body of current information processing and information resource construction. Traditional technical means have been unable to meet this demand, which poses new challenges to technologies such as image organization, analysis, retrieval and management. Image classification, as the basic technology for machine understanding of images, has been a continuous frontier research hotspot in many important research fields such as pattern recognition, computer vision, information retrieval, artificial intelligence, machine learning and data mining in recent years. Image classification refers to the method of classifying images into specific semantic categorie...

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): G06K9/62
Inventor 杨育彬唐晔潘玲燕
Owner NANJING 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