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Encoder-Decoder Based Multi-Feature Fusion Model for Image Caption Generation

Mingyang Duan, Jin Liu*, Shiqi Lv

Shanghai Maritime University, Shanghai, 201306, China

* Corresponding Author: Jin Liu. Email: email

Journal on Big Data 2021, 3(2), 77-83.


Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in the traditional Encoder-Decoder architectures, only the global features of the images are extracted, while the local information of the images is not well utilized. This paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption text. We use VGG16 and Faster R-CNN to extract global and local features in the encoder first. Then, we train the bidirectional LSTM network with the fused features in the decoder. Finally, the local features extracted is used to correct the caption text. The experiment results prove that the effectiveness of the proposed method.


Cite This Article

M. Duan, J. Liu and S. Lv, "Encoder-decoder based multi-feature fusion model for image caption generation," Journal on Big Data, vol. 3, no.2, pp. 77–83, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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