Object recognition based on generalized linear regression classification in use of color information

Yang Ting Chou, Jar Ferr Kevin Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Limited size of object images and lack amount of training data would degrade the performance seriously in modern-day recognition applications. Therefore, how to effectively utilize available information from images becomes more and more important. In this paper, we propose to extend the linear regression classification (GLRC), which can effectively use all the information in cases of multiple inputs, e.g. R, G, and B color components. Experimental results for SOIL-47 object dataset and SDUMLA-HMT face database show that the proposed GLRC method with R, G, and B channels performs better than the original LRC and contemporary popular methods.

Original languageEnglish
Title of host publication2014 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages272-275
Number of pages4
EditionFebruary
ISBN (Electronic)9781479952304
DOIs
Publication statusPublished - 2015 Feb 5
Event2014 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2014 - Ishigaki Island, Okinawa, Japan
Duration: 2014 Nov 172014 Nov 20

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS
NumberFebruary
Volume2015-February

Other

Other2014 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2014
Country/TerritoryJapan
CityIshigaki Island, Okinawa
Period14-11-1714-11-20

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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