Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Dynamic texture recognition method based on multi-task learning

A multi-task learning, dynamic texture technology, applied in character and pattern recognition, instruments, computer parts and other directions, can solve problems such as complex computing, and achieve the effect of improving learning efficiency

Inactive Publication Date: 2018-01-09
苏州珂锐铁电气科技有限公司
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] However l 0 The calculation of the norm is very complicated

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
  • Dynamic texture recognition method based on multi-task learning
  • Dynamic texture recognition method based on multi-task learning
  • Dynamic texture recognition method based on multi-task learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0056] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0057] Previous recognition methods, such as sparse representation methods, learn each histogram feature, which can be regarded as a single-task learning process. Simultaneously solving sparse representations for multiple training features can be seen as a multi-task learning process. Based on this idea, the present invention proposes to use the dirty model to solve, such as Figure 5 shown. Decompose the total model W into group sparse model parts l 1，∝ and element sparse part l 1，1 . The group sparse model part can obtain the common features between multiple tasks, while the element sparse model can obtain the individual parts of each feature. Therefore, the mixtur...

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 specifically relates to a dynamic texture recognition method based on multi-task learning and is designed so as to improve dynamic texture recognition accuracy. The dynamic texture recognition method based on multi-task learning is characterized by, to begin, carrying out chaotic feature vector extraction on each pixel time sequence in a dynamic texture video, so that the video is changed into a chaotic feature vector matrix; then, carrying out video modeling through a bag-of-word model to obtain histogram features; and converting the identification problem into group sparse representation, and calculating through an ADMM method. The method can carry out dynamic texture recognition through a multi-task learning method, can be widely applied to various civilian and military systems, such as a video monitoring system, a video conference system, an industrial product detection system, a robot vision navigation system and a military target detection and classification system,and has a wide market prospect and application value.

Description

technical field [0001] The invention relates to the technical field of computer pattern recognition, in particular to a dynamic texture recognition method based on multi-task learning. Background technique [0002] In recent years, sparse representations have received extensive attention in pattern recognition. Literature (J, Wright, A, Y, Yang, A, Ganesh, S, S, Sastry, Y, Ma, Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2), 210 –227.) proposed to use sparse representation for face recognition, and compared with the nearest neighbor and support vector machine recognition methods. From the experimental results, the sparse representation is better than the nearest neighbor and support vector machine for face recognition, and it can also achieve a better recognition rate in the case of noise and occlusion. Sparse representations are successfully used in applications such as image recognition, objec...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 洪金剑王勇
Owner 苏州珂锐铁电气科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com