Name
Abstract | ||
|---|---|---|
Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable. However, a clear understanding and reliable estimation of natural scene memorability remain elusive. In this paper, we provide an attempt to answer: "what exactly makes natural scene memorable''. Specifically, we first build LNSIM, a large-scale natural scene image memorability database (containing 2,632 images and memorability annotations). Then, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of natural scene. In particular, we find that high-level feature of scene category is rather correlated with natural scene memorability. Thus, we propose a deep neural network based natural scene memorability (DeepNSM) predictor, which takes advantage of scene category. Finally, the experimental results validate the effectiveness of DeepNSM.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3267799.3267802 | MM '18: ACM Multimedia Conference
Seoul
Republic of Korea
October, 2018 |
Keywords | Field | DocType |
Image memorability, Natural scene, Computer vision | Computer vision,Computer science,Artificial intelligence,Artificial neural network,Machine learning | Journal |
Volume | ISSN | ISBN |
abs/1808.08754 | Proceedings of the 2018 Workshop on Understanding Subjective
Attributes of Data, with the Focus on Evoked Emotions | 978-1-4503-5978-8 |
Citations | PageRank | References |
1 | 0.35 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaxin Lu | 1 | 1 | 0.68 |
| Mai Xu | 2 | 509 | 57.90 |
| Ren Yang | 3 | 64 | 8.19 |
| Zulin Wang | 4 | 216 | 29.63 |