Image clustering using multi-visual features

Bilih Priyogi, Nungki Selviandro, Zainal Arifin Hasibuan, Mubarik Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

This paper presents a research on clustering an image collection using multi-visual features. The proposed method extracted a set of visual features from each image and performed multi-dimensional K-Means clustering on the whole collection. Furthermore, this work experiments on different number of visual features combination for clustering. 2, 3, 5 and 7 pair of visual features chosen from a total of 8 visual features used, to measure the impact of using more visual features towards clustering performance. The result show that the accuracy of multi-visual features clustering is promising, but using too many visual features might set a drawback.

Original languageEnglish
Title of host publicationInformation and Communication Technology - Second IFIP TC5/8 International Conference, ICT-EurAsia 2014, Proceedings
PublisherSpringer Verlag
Pages179-189
Number of pages11
ISBN (Print)9783642550317
DOIs
Publication statusPublished - 1 Jan 2014
Event2nd IFIP TC5/8 International Conference on Information and Communication Technology, ICT-EurAsia 2014 - Bali, Indonesia
Duration: 14 Apr 201417 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8407 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd IFIP TC5/8 International Conference on Information and Communication Technology, ICT-EurAsia 2014
Country/TerritoryIndonesia
CityBali
Period14/04/1417/04/14

Keywords

  • Image Clustering
  • K-Means Clustering
  • Visual Feature

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