Automatic Semantic Annotation of Images using Spatial Hidden Markov Model

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.

• Type: Conference
• Year: 2006
• Where: ICMCS
• Authors: Feiyang Yu, Horace Ho-Shing Ip
• Keywords: 2D Generalization, HMM-liked Model, ICMCS 2006, Model