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ResMem-Net: memory based deep CNN for image memorability estimation
- Source :
- PeerJ Computer Science, Vol 7, p e767 (2021), PeerJ Computer Science
- Publication Year :
- 2021
- Publisher :
- PeerJ Inc., 2021.
-
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.
- Subjects :
- Deep cnn
Saliency
General Computer Science
Mean squared error
Computer science
business.industry
Deep learning
Computer Vision
Data Mining and Machine Learning
Data Science
Image Memorability
Pattern recognition
Image processing
QA75.5-76.95
Net (mathematics)
Image (mathematics)
Consistency (database systems)
Deep Learning
Artificial Intelligence
Electronic computers. Computer science
Object Interestingness
Artificial intelligence
Architecture
business
Visual Emotions
Subjects
Details
- Language :
- English
- ISSN :
- 23765992
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- PeerJ Computer Science
- Accession number :
- edsair.doi.dedup.....a34b0b2a9b9f7668b2dae801b8a35591