Automatic Image Annotation Based on Generalized Relevance Models |
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Authors: | Zhiwu Lu Horace H S Ip |
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Affiliation: | (1) Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China |
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Abstract: | This paper presents a generalized relevance model for automatic image annotation through learning the correlations between
images and annotation keywords. Different from previous relevance models that can only propagate keywords from the training
images to the test ones, the proposed model can perform extra keyword propagation among the test images. We also give a convergence
analysis of the iterative algorithm inspired by the proposed model. Moreover, to estimate the joint probability of observing
an image with possible annotation keywords, we define the inter-image relations through proposing a new spatial Markov kernel
based on 2D Markov models. The main advantage of our spatial Markov kernel is that the intra-image context can be exploited
for automatic image annotation, which is different from the traditional bag-of-words methods. Experiments on two standard
image databases demonstrate that the proposed model outperforms the state-of-the-art annotation models. |
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Keywords: | |
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