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Conference Papers Year : 2016

Automatic Image Annotation Based on Semi-supervised Probabilistic CCA

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Abstract

We propose a novel semi-supervised method for building a statistical model that represents the relationship between images and text labels (tags) based on a semi-supervised variant of CCA called SemiPCCA, which extends the probabilistic CCA model to make use of the labelled and unlabelled images together to extract the low-dimensional latent space representing topics of images. Real-world image tagging experiments indicate that our proposed method improves the accuracy even when only a small number of labelled images are available.
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Dates and versions

hal-01614988 , version 1 (11-10-2017)

Licence

Attribution - CC BY 4.0

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Cite

Bo Zhang, Gang Ma, Xi Yang, Zhongzhi Shi, Jie Hao. Automatic Image Annotation Based on Semi-supervised Probabilistic CCA. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.211-221, ⟨10.1007/978-3-319-48390-0_22⟩. ⟨hal-01614988⟩
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