Aggregate metric networks for nearest neighbour classifiers

Dhanya Alex, Alex Pappachen James

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


A common idea prevailing in distance or similarity measures is the use of aggregate operators in localised and point-wise differences or similarity calculation between two patterns. We test the impact of aggregate operations such as min, max, average, sum, product, sum of products and median on distance measures and similarity measures for nearest neighbour classification. The point-wise differences or similarities extends the idea of distance measurements from the decision space to feature space for the extraction of inter-feature dependencies in high dimensional patterns such as images. Inter-feature spatial differences are extracted using the gradient functions across various directions and then applied on aggregate function, to result in a fused feature set. The initial study is conducted on Iris flower and verified using AR face database. The resulting method shows an accuracy of 92% on face recognition task using the standard AR database.

Original languageEnglish
Title of host publicationAdvances in Signal Processing and Intelligent Recognition Systems
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319049595
Publication statusPublished - Jan 1 2014
EventInternational Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2014 - Trivandrum, India
Duration: Mar 13 2014Mar 15 2014

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


OtherInternational Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2014

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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