Using neuro-fuzzy techniques based on a two-stage mapping model for concept-based image database indexing

Chih Fong Tsai, K. McGarry, J. Tait

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

5 Scopus citations

Abstract

We present a two-stage mapping model (TSMM), which is intended to minimise the semantic gap for content-based image retrieval (CBIR) by reducing recognition errors during the image indexing stage. This model is composed of a feature extraction module based on our image segmentation and feature extraction algorithm, a colour and texture classification modules based on support vector machines (SVMs), and an inference module based on fuzzy logic to make final decisions as high level concepts from the colour and texture concepts. The experimental results show that the proposed method outperforms general approaches by using one single SVM classifier as direct mapping between the combined colour and texture feature vectors and high level concepts directly.

Original languageEnglish
Title of host publicationProceedings - IEEE 5th International Symposium on Multimedia Software Engineering, ISMSE 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6-12
Number of pages7
ISBN (Electronic)0769520316, 9780769520315
DOIs
StatePublished - 2003
Event5th IEEE International Symposium on Multimedia Software Engineering, ISMSE 2003 - Taichung, Taiwan
Duration: 10 Dec 200312 Dec 2003

Publication series

NameProceedings - IEEE 5th International Symposium on Multimedia Software Engineering, ISMSE 2003

Conference

Conference5th IEEE International Symposium on Multimedia Software Engineering, ISMSE 2003
Country/TerritoryTaiwan
CityTaichung
Period10/12/0312/12/03

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