Abstract
This paper proposes an automatic image annotation method based on concept-specific image representation and discriminative learning. Firstly, the concept-specific visual vocabularies are generated by assuming that localized features from the images with a specific concept are of the distribution of Gaussian Mixture Model (GMM). Each component in the GMM is taken as a visual token of the concept. The visual tokens of all the concepts are clustered to obtain a universal token set. Secondly, the image is represented as a concept-specific feature vector by computing the average posterior probabilities of being each universal visual token for all the localized features and assigning it to corresponding concept-specific visual tokens. Thus the feature vector for an image varies with different concepts. Finally, we implement image annotation and retrieval under a discriminative learning framework of Bayesian classifiers, Max-Min posterior Pseudo-probabilities (MMP). The proposed method were evaluated on the popular Corel-5K database. The experimental results with comparisons to state-of-the-art show that our method is promising.
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Wang, Y., Liu, X., Jia, Y. (2010). Automatic Image Annotation with Cooperation of Concept-Specific and Universal Visual Vocabularies. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_28
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DOI: https://doi.org/10.1007/978-3-642-11301-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
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