Abstract
In Bag-of-Visual-Words (BoVW) framework, there is lacking of the semantic relatedness between visual words. Therefore, a visual word embeddings approach has been proposed in this paper, which is similar to the word embedding technique in natural language processing (NLP). First of all, a large number of visual words are extracted and collected from a word image collection under the framework of BoVW. And then, a deep learning procedure is used for mapping visual words into embedding vectors in a semantic space. After that, the visual word embeddings are integrated into a translation language model for attaining the aim of keyword spotting in the scenario of query-by-example. Experimental results prove that the proposed visual word embeddings based translation language model approach for keyword spotting outperforms various state-of-the-art methods, including BoVW, language model (LM), translation language model with mutual information (TLM-MI) and latent Dirichlet allocation (LDA).
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The paper is supported by the National Natural Science Foundation of China under Grant 61463038.
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Wei, H., Zhang, H., Gao, G. (2018). Integrating Visual Word Embeddings into Translation Language Model for Keyword Spotting on Historical Mongolian Document Images. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_60
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