Automatic Image Tagging via a Generative Probabilistic Model

Abstract:

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We present an approach to tag image automatically via visual topic detecting and initial annotations expanding. Visual topics are detected from corel5k dataset by probabilistic latent semantic analysis (PLSA) model. For an image which is to be tagged, PLSA is used to find visual topic of this image, and then construct initial annotations set. After initial annotations are generated, we use a weighted voting scheme and Flickr API to expand initial annotations. After the above two process, we combine initial annotations and expanded annotations together to construct final annotations. From experimental results, the conclusions can be draw that our PLSA based image tagging approach works effectively.

Info:

Periodical:

Edited by:

Ran Chen

Pages:

3443-3447

DOI:

10.4028/www.scientific.net/AMM.44-47.3443

Citation:

Z. Liu "Automatic Image Tagging via a Generative Probabilistic Model", Applied Mechanics and Materials, Vols. 44-47, pp. 3443-3447, 2011

Online since:

December 2010

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Price:

$35.00

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