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Automatic Image Annotation and Retrieval Using Hybrid Approach

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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https://hal.inria.fr/hal-01524985
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Zhixin Li, Weizhong Zhao, Zhiqing Li, Zhiping Shi. Automatic Image Annotation and Retrieval Using Hybrid Approach. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. pp.347-356, ⟨10.1007/978-3-642-32891-6_43⟩. ⟨hal-01524985⟩

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