Citation

Chew, Yan Han and Wong, Lai Kuan and See, John Su Yang and Khor, Huai Qian and Abivishaq, Balasubramanian (2019) LiteEmo: Lightweight Deep Neural Networks for Image Emotion Recognition. In: 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), 27-29 Sept. 2019, Kuala Lumpur, Malaysia.

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Abstract

Psychology studies have shown that an image can invoke various emotions, depending on the visual features as well as semantic content of the image. Ability to identify image emotion can be very useful for many applications, including image retrieval and aesthetics prediction. Notably, most of the existing deep learning-based emotion recognition models do not capitalize on additional semantics or contextual information and are computational expensive. Inspired to overcome these limitations, we proposed a lightweight multi-stream deep network that concatenates several MobileNet networks for performing image emotion analysis. Each stream in the multi-stream deep network represents the core emotion recognition, object recognition and image category recognition models respectively. Experimental results demonstrate the effectiveness of the additional contextual information in producing comparable performance as the state-of-the-art emotion models, but with lesser parameters, thus improving its practicality.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Image emotion, lightweight, multi-stream network
Subjects:	Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Oct 2021 04:27
Last Modified:	27 Oct 2021 04:27
URII:	http://shdl.mmu.edu.my/id/eprint/8826

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