  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.
