Abstract
We firstly propose continuous probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, we present a hybrid framework which employs continuous PLSA to model visual features of images in generative learning stage and uses ensembles of classifier chains to classify the multi-label data in discriminative learning stage. Since the framework combines the advantages of generative and discriminative learning, it can predict semantic annotation precisely for unseen images. Finally, we conduct a series of experiments on a standard Corel dataset. The experiment results show that our approach outperforms many state-of-the-art approaches.
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Li, Z., Zhao, W., Li, Z., Shi, Z. (2012). Automatic Image Annotation and Retrieval Using Hybrid Approach. In: Shi, Z., Leake, D., Vadera, S. (eds) Intelligent Information Processing VI. IIP 2012. IFIP Advances in Information and Communication Technology, vol 385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32891-6_43
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DOI: https://doi.org/10.1007/978-3-642-32891-6_43
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