Automatic semantic annotation of images using spatial hidden Markov model

Feiyang Yu, Horace H. S. Ip

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

18 Citations (Scopus)

Abstract

This paper presents a new spatial-HMM(SHMM)for automatically classifying and annotating natural images. Our model is a 2D generalization of the traditional HMM in the sense that both vertical and horizontal transitions between hidden states are taken into consideration. The three basic problems with HMM-liked model are also solved in our model. Given a sequence of visual features, our model automatically derives annotations from keywords associated with the most appropriate concept class, and with no need of a pre-defined length threshold. Our experiments showed that our model outperformed the previous 2D MHMM in recognition accuracy and also achieved a high annotation accuracy. © 2006 IEEE.
Original languageEnglish
Title of host publication2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
Pages305-308
Volume2006
DOIs
Publication statusPublished - 2006
Event2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Toronto, ON, Canada
Duration: 9 Jul 200612 Jul 2006

Publication series

Name
Volume2006

Conference

Conference2006 IEEE International Conference on Multimedia and Expo, ICME 2006
Country/TerritoryCanada
CityToronto, ON
Period9/07/0612/07/06

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