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Open Access

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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:

Journal on Big Data 2021, 3(2), 77-83. https://doi.org/10.32604/jbd.2021.016674

Received 08 January 2021; Accepted 07 April 2021; Issue published 13 April 2021

Abstract

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.

Keywords

Image understanding; image captioning; deep learning; fused features

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.



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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