Digital media object classification method based on large margin distributed learning

A technology of digital media and classification methods, which is applied in the fields of electronic digital data processing, multimedia data retrieval, character and pattern recognition, etc., can solve the problems of noise sensitivity and is not suitable for classification of digital media objects, and achieves overcoming noise problems and good classification effect. Effect

Active Publication Date: 2014-10-15
NANJING UNIV
View PDF4 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional classification methods based on large intervals, such as support vector machines (hereinafter abbreviated as SVM), are sensitiv

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
  • Digital media object classification method based on large margin distributed learning
  • Digital media object classification method based on large margin distributed learning
  • Digital media object classification method based on large margin distributed learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0010] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0011] Such as figure 1 As shown, the classification method of digital media objects based on large-interval distribution learning, first, the user prepares a digital media object library, and for each digital media object in it, obtains the corresponding category mark by labeling or crowdsourcing method, forming training data. Next, convert the training digital media object into a feature representation, specifically, input the training digital media object into a feature extraction algorithm to...

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 a digital media object classification method based on large margin distributed learning and aims to solve the problem that marking of classes of digital media causes noise. The form of the classification problem of the digital media objects is finally reduced into a convex secondary optimization problem by maximizing a margin average and minimizing a margin variance; two optimization algorithms which are based on dual coordinate descent and average stochastic gradient descent respectively are achieved according to whether or not to use a nonlinear kernel function and according to features of a training digital media object base; users can make selections on their own according to actual conditions; if the users select the nonlinear kernel function, the DCD (dual coordinate descent) is selected as the optimization algorithm for training; if the users select a linear kernel function, the training digital media object base provide many samples or has few features, the ASGD (average stochastic gradient descent) is selected as the optimization algorithm for training, and if not, the DCD is still selected as the optimization algorithm.

Description

technical field [0001] The invention relates to a digital media object classification method, in particular to a digital media object classification method based on large interval distribution learning. Background technique [0002] The current human society has entered the stage of digitalization in an all-round way. Currently, images, texts, videos, audios and other media used to disseminate information are recorded and processed in the form of binary codes. These encoded images, texts, videos, audios Collectively referred to as Digital Media Objects. Digital media objects have been widely used in all walks of life, such as remote sensing measurement and control, Internet sites, digital TV, telephone communications, etc. These industries accumulate a large amount of data every day, so as the amount of data continues to expand, how to effectively organize and manage digital media objects becomes more and more important, and the core issue is the classification of digital m...

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): G06F17/30
CPCG06F16/40G06F18/24
Inventor 周志华张腾
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