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.
This paper presents a new spatial-HMM for automatically classifying and annotating histological images. Our model is a 2D generalization of HMM. Given a matrix of feature vectors for all blocks in an image, the most appropriate semantic labels determined by our models are used for annotation. Our experimental results showed that our model is superior to HMM in both recognition and annotation accuracy.
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