Understanding and Predicting the Memorability of Outdoor Natural Scenes

TL;DR

This paper introduces a large-scale database and a deep learning model to predict outdoor natural scene memorability, highlighting the importance of scene category and deep features for accurate predictions.

Contribution

The paper creates the first large-scale outdoor natural scene memorability database and proposes an end-to-end deep neural network model that outperforms existing methods.

Findings

High-level scene category features are strongly correlated with memorability.

Deep neural network features effectively predict memorability scores.

Combining category features with deep features improves prediction accuracy.

Abstract

Memorability measures how easily an image is to be memorized after glancing, which may contribute to designing magazine covers, tourism publicity materials, and so forth. Recent works have shed light on the visual features that make generic images, object images or face photographs memorable. However, these methods are not able to effectively predict the memorability of outdoor natural scene images. To overcome this shortcoming of previous works, in this paper, we provide an attempt to answer: "what exactly makes outdoor natural scenes memorable". To this end, we first establish a large-scale outdoor natural scene image memorability (LNSIM) database, containing 2,632 outdoor natural scene images with their ground truth memorability scores and the multi-label scene category annotations. Then, similar to previous works, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of outdoor natural scenes. In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores. Moreover, combining the deep features with the category feature can further boost the performance of memorability prediction. Therefore, we propose an end-to-end DNN based outdoor natural scene memorability (DeepNSM) predictor, which takes advantage of the learned category-related features. Then, the experimental results validate the effectiveness of our DeepNSM model, exceeding the state-of-the-art methods. Finally, we try to understand the reason of the good performance for our DeepNSM model, and also study the cases that our DeepNSM model succeeds or fails to accurately predict the memorability of outdoor natural scenes. Code: github.com/JiaxinLu-home/Natural-Scene-Memorability-Dataset.