Comparison of image partition methods for adaptive image categorization based on structural image representation

Zhiyong Wang, David Feng, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Image categorization is very helpful for organizing large image databases efficiently, however, it is yet very challenging due to lack of effective image representations. Our previous work showed that structural representations were good at characterizing image contents, since image contents could be exploited from coarse to fine scales through the structures representation and fewer visual features are required. In this paper, several popular image partition methods are investigated for adaptive image categorization based on structural representation. Experimental results on seven categories of scenery images show that both the structure and node attributes are important to categorize image contents. In addition, the more similar the structures of each category, the better the categorization performance.
Original languageEnglish
Title of host publication2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Pages676-680
Number of pages5
Volume1
Publication statusPublished - 1 Dec 2004
Event8th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Kunming, China
Duration: 6 Dec 20049 Dec 2004

Conference

Conference8th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Country/TerritoryChina
CityKunming
Period6/12/049/12/04

ASJC Scopus subject areas

  • Engineering(all)

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