Understanding Image Memorability through Localized Stimuli
Publish place: Journal of Modeling & Simulation in Electrical & Electronics Engineering، Vol: 3، Issue: 2
Publish Year: 1402
Type: Journal paper
Language: English
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Document National Code:
JR_MSEEE-3-2_001
Index date: 21 October 2024
Understanding Image Memorability through Localized Stimuli abstract
In today's digital age, we are bombarded with images from the internet, social media, and online magazines. It is fascinating how we can remember so many of these images and their details. However, not every image is equally memorable; some stay with us more than others. Scientists have explored why this is the case. In our research, we are particularly interested in how images that showcase Iranian life and culture stick in the memories of Iranian adults. To investigate this, we created a new collection called the SemMem dataset, which is full of culturally relevant images. We adapted a memory game from earlier studies to test how memorable these images are. To analyze memorability, we used two deep learning architectures, ResNet 50 and ResNet 101. These architectures helped us estimate which images are likely to be remembered. Our findings confirmed that images connected to Iranian culture are indeed more memorable to Iranians, highlighting the impact of familiar cultural elements on memory retention.
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Understanding Image Memorability through Localized Stimuli authors
Amir Shokri
Electrical & Computer Engineering Department, Semnan University, Semnan, Iran.
Farzin Yaghmaee
Faculty of Electrical and Computer Engineering (ECE), Semnan University, Semnan, Iran.
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