A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

Understand

Traditional saliency models usually adopt hand-crafted image features and human-designed mechanisms to calculate local or global contrast.

• In this paper, we propose a novel computational saliency model, i.e., deep spatial contextual long-term recurrent convolutional network (DSCLRCN) to predict where people looks in natural scenes.
• DSCLRCN first automatically learns saliency related local features on each image location in parallel.
• Then, in contrast with most other deep network based saliency models which infer saliency in local contexts, DSCLRCN can mimic the cortical lateral inhibition mechanisms in human visual system to incorporate global contexts to assess the saliency of each image location by leveraging the deep spatial long short-term memory (DSLSTM) model.