ResMem-Net: memory based deep CNN for image memorability estimation

Author:

Praveen Arockia1,Noorwali Abdulfattah2,Samiayya Duraimurugan3,Zubair Khan Mohammad4,Vincent P M Durai Raj5,Bashir Ali Kashif6,Alagupandi Vinoth3

Affiliation:

1. Phosphene AI, Madurai, India

2. Umm Al-Qura University, Makkah, Saudi Arabia

3. Optisol Business Solutions, Chennai, India

4. Department of Computer Science, Taibah University, Medina, Saudi Arabia

5. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

6. The Manchester Metropolitan University, Manchester, United Kingdom

Abstract

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: “What makes an image memorable?”. The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production.

Funder

Umm Al-Qura University

Publisher

PeerJ

Subject

General Computer Science

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