Novel EFM-KNN classifier and a new color descriptor for image classification

Abhishek Verma, Chengjun Liu

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

6 Scopus citations

Abstract

We propose a new CGSFPHOG descriptor and perform image classification using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. We integrate the oRGB-SIFT descriptor with other color SIFT features to produce the Color SIFT Fusion (CSF) and the Color Grayscale SIFT Fusion (CGSF) descriptors. The CGSF is integrated to the PHOG to obtain the novel CGSFPHOG descriptor. The effectiveness of the proposed new descriptor and the classification method is evaluated using two grand challenge datasets: the Oxford flower database and the MIT scene database. The classification results using the EFM-KNN classifier show that (i) the CGSFPHOG descriptor improves recognition performance upon other descriptors; and (ii) the oRGB-SIFT, the CSF, and the CGSF perform better than the other color SIFT descriptors.

Original languageEnglish (US)
Title of host publicationWOCC 2011 - 20th Annual Wireless and Optical Communications Conference
DOIs
StatePublished - 2011
Event20th Annual Wireless and Optical Communications Conference, WOCC 2011 - Newark, NJ, United States
Duration: Apr 15 2011Apr 16 2011

Publication series

NameWOCC 2011 - 20th Annual Wireless and Optical Communications Conference

Other

Other20th Annual Wireless and Optical Communications Conference, WOCC 2011
CountryUnited States
CityNewark, NJ
Period4/15/114/16/11

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Communication

Keywords

  • CGSFPHOG descriptor
  • Color Grayscale SIFT Fusion (CGSF)
  • Color SIFT Fusion (CSF)
  • EFM-KNN classifier
  • image classification
  • oRGB-SIFT descriptor

Fingerprint Dive into the research topics of 'Novel EFM-KNN classifier and a new color descriptor for image classification'. Together they form a unique fingerprint.

Cite this