Abstract
In this paper, we describe our experiments using Latent Dirichlet Allocation (LDA) to model images containing both perceptual features and words. To build a large-scale image tagging system, we distribute the computation of LDA parameters using MapReduce. Empirical study shows that our scalable LDA supports image annotation both effectively and efficiently. © 2008 Springer.
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CITATION STYLE
Liu, J., Hu, R., Wang, M., Wang, Y., & Chang, E. Y. (2008). Web-scale image annotation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5353 LNCS, pp. 663–674). https://doi.org/10.1007/978-3-540-89796-5_68
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