Automatic Image Tagging via a Generative Probabilistic Model

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 3443-3447
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.3443
• Online since: December 2010
• Authors: Zheng Liu
• Keywords: Automatic Image Tagging, PLSA, SIFT, Visual Word Model

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