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Image captioning: from structural tetrad to translated sentences

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Abstract

Generating semantic descriptions for images becomes more and more prevalent in recent years. Sentence which contains objects with their attributes and activity or scene involved is more informative and able to express more details of image semantic. In this paper, we focus on the generation of descriptions for images from the structural words we have generated, i.e., a semantically-layered structural tetrad of <object, attribute, activity, scene>. We propose to use deep machine translation method to generate semantically meaningful descriptions. In particular, the generated sentences describe objects with attributes, such as color, size, and corresponding activities or scenes involved. We propose to use a multi-task learning method to recognize structural words. Taking the words sequence as source language, we train a LSTM encoder-decoder machine translation model to output the target caption. In order to demonstrate the effectiveness of using multi-task learning method to generate structural words, we do experiments on benchmark datasets, i.e., aPascal and aYahoo. We also use UIUC Pascal, Flickr8k, Flickr30k, and MSCOCO datasets to justify that translating structural words to sentences achieves promising performance compared to the state-of-the-art methods of image captioning in terms of language generation metrics.

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Acknowledgements

This work is supported by the NSFC (under Grant U1509206,61472276, 61876130) and Tianjin Natural Science Foundation (no. 15JCYBJC15400).

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Authors and Affiliations

  1. National Engineering Research Center of Turbo Generator Vibration, School of Energy and Environment, Southeast University, Nanjing, 210096, China

    Rui Guo

  2. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Shubo Ma & Yahong Han

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  1. Rui Guo
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Correspondence to Yahong Han.

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Guo, R., Ma, S. & Han, Y. Image captioning: from structural tetrad to translated sentences. Multimed Tools Appl 78, 24321–24346 (2019). https://doi.org/10.1007/s11042-018-7118-7

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