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.
Isola, P., Parikh, D., Torralba, A., Oliva, A, "Understanding the intrinsic memorability of images," Advances in neural information processing systems, p. 2429–2437, 2011.
Phillip Isola, Jianxiong Xiao, Antonio Torralba, Aude Oliva, "What makes an image memorable?," in CVPR 2011, 2011.
Nicole C. Rust, Vahid Mehrpour, "Understanding Image Memorability," Trends in Cognitive Sciences, p. 12, 2020.
Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A., "What makes a photograph memorable?," in Pattern Analysis and Machine Intelligence, 2014.
Lore Goetschalckx and Johan Wagemans, "MemCat: A new category-based image set quantified on memorability," PeerJ, 2019.
Zoya Bylinskii, Lore Goetschalckx, Anelise Newman, Aude Oliva, "Memorability: An image-computable measure of information utility," 2021.
Spearman, "Correlation calculated from faulty data," British Journal of Psychology, p. 271–295, 1910.
Bainbridge, W.A., Isola, P., Oliva, A, "The intrinsic memorability of face photographs," Journal of Experimental Psychology, 2013.
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A., "Sun database: Large-scale scene recognition from abbey to zoo," in computer society conference on computer vision and pattern recognition, 2010.
Borkin, M.A., Vo, A.A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., Pfister, H, "WhattWhat makes a visualization memorable?," in Visualization and Computer Graphics, 2013.
Bylinskii, Z., Isola, P., Bainbridge, C., Torralba, A., Oliva, "Intrinsic and extrinsic effects on image memorability," Vision research, p. 165–178, 2015.
Dubey, R., Peterson, J., Khosla, A., Yang, M.H., Ghanem, B, "What makes an object memorable" in the pieceeie international conference on computer vision, 2015.
Akagunduz, E., Bors, A., Evans, K, "Defining image memorability using the visual memory schema," in pattern analysis and machine intelligence, 2019.
Lu, J., Xu, M., Yang, R., Wang, Z, "Understanding and predicting the memorability of outdoor natural scenes," p. 4927–494, 2020.
Khosla, A., Raju, A.S., Torralba, A., Oliva, "Understanding and predicting image memorability at a large scale," in Proceedings of the IEEE International Conference on Computer, 2015.
He, K., Zhang, X., Ren, S., Sun, J, "Deep residual learning for image recognition," in The IEEE ConferenceConference on Computer Vision and pattern recognition, 2015.
Deng, J., Dong, W., Socher, R., Li, L., Kai Li, Li Fei-Fei, "Imagenet: A large-scale hierarchical image database," in IEEE Conference on Computer Vision and Pattern Recognition, 2009.
Võ, M.L.H., Bylinskii, Z., Oliva, A, "Image memorability in the eye of the beholder: Tracking the decay of visual scene representations," bioRxiv, 2017.
Perera, S., Tal, A., Zelnik-Manor, L., "Is image memorability prediction solved?," in Computer Vision and Pattern Recognition Workshops, 2019.
Cohendet, R., Demarty, C., Duong, N.Q.K., Martin, E, "VideoMem: Constructing, Analyzing, Predicting Short-Term and Long-Term Video Memorability," in international conference on computer vision, 2019.
Cohendet, R., Yadati, K., Duong, N.Q., Demarty, C.H, "Annotating, Understanding, and Predicting Long-Term Video Memorability," in International Conference on Multimedia Retrieval, 2018.
Newman, A., Fosco, C., Casser, V., Lee, A., Barry, Mcnamara, Oliva, A, "Multimodal morability: Modeling effects of semantics and decay on video memorability," in ECCV, 2020.
Shokri, A. and Yaghmaee, F. (2023). Understanding Image Memorability through Localized Stimuli. Modeling and Simulation in Electrical and Electronics Engineering, 3(2), 1-6. doi: 10.22075/mseee.2024.33159.1143
MLA
Shokri, A. , and Yaghmaee, F. . "Understanding Image Memorability through Localized Stimuli", Modeling and Simulation in Electrical and Electronics Engineering, 3, 2, 2023, 1-6. doi: 10.22075/mseee.2024.33159.1143
HARVARD
Shokri, A., Yaghmaee, F. (2023). 'Understanding Image Memorability through Localized Stimuli', Modeling and Simulation in Electrical and Electronics Engineering, 3(2), pp. 1-6. doi: 10.22075/mseee.2024.33159.1143
CHICAGO
A. Shokri and F. Yaghmaee, "Understanding Image Memorability through Localized Stimuli," Modeling and Simulation in Electrical and Electronics Engineering, 3 2 (2023): 1-6, doi: 10.22075/mseee.2024.33159.1143
VANCOUVER
Shokri, A., Yaghmaee, F. Understanding Image Memorability through Localized Stimuli. Modeling and Simulation in Electrical and Electronics Engineering, 2023; 3(2): 1-6. doi: 10.22075/mseee.2024.33159.1143
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