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 Semi- PCCA, 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.
CITATION STYLE
Zhang, B., Ma, G., Yang, X., Shi, Z., & Hao, J. (2016). Automatic image annotation based on semi-supervised probabilistic CCA. In IFIP Advances in Information and Communication Technology (Vol. 486, pp. 211–221). Springer New York LLC. https://doi.org/10.1007/978-3-319-48390-0_22
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