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RETRACTED ARTICLE：A generative hierarchical topic model for image annotation
- 1 Xiamen University
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Abstract
A redesigned hierarchical model for image annotation is presented in this study, which can also be simplified to image classification. Based on some previous models for textual analysis, such as LDA and the hierarchical clustering model by Hoffman et al. in 1988, the author applies similar ideas to image data, extracting image features as words and setting the latent variables of topics. Compared with the popular annotation methods which are deep-learning-based, the model put forward in this document details the generating process of images by adding the layer of topics. Besides, compared to other hierarchical models such as the multi-modal hierarchical model by Barnard and Forsyth in 2001, the model in this paper simplifies it to a single-modal one and simultaneously achieves similar effects. We collect images under different human activities, such as riding horses, playing the guitar, etc., mainly from Google search engine and the result proves that our model works better than most previous probabilistic models.
Keywords
Image annotation, topic model, LDA, variational EM
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Cite this article
Wang,X. (2024). RETRACTED ARTICLE：A generative hierarchical topic model for image annotation. Applied and Computational Engineering,88,9-11.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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