Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks
Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks
复制标题
学习通过多分辨率卷积神经网络预测眼睛注视
DOI：
10.1109/tnnls.2016.2628878
复制
发表时间：
2018-02-01
影响因子：
10.4
通讯作者：
Li, Xuelong
中科院分区：
文献类型：
--
作者：
Liu, Nian;Han, Junwei;Li, Xuelong
Eye movements in the case of freely viewing natural scenes are believed to be guided by local contrast, global contrast, and top-down visual factors. Although a lot of previous works have explored these three saliency cues for several years, there still exists much room for improvement on how to model them and integrate them effectively. This paper proposes a novel computation model to predict eye fixations, which adopts a multiresolution convolutional neural network (Mr-CNN) to infer these three types of saliency cues from raw image data simultaneously. The proposed Mr-CNN is trained directly from fixation and nonfixation pixels with multiresolution input image regions with different contexts. It utilizes image pixels as inputs and eye fixation points as labels. Then, both the local and global contrasts are learned by fusing information in multiple contexts. Meanwhile, various top-down factors are learned in higher layers. Finally, optimal combination of top-down factors and bottom-up contrasts can be learned to predict eye fixations. The proposed approach significantly outperforms the state-of-the-art methods on several publically available benchmark databases, demonstrating the superiority of Mr-CNN. We also apply our method to the RGB-D image saliency detection problem. Through learning saliency cues induced by depth and RGB information on pixel level jointly and their interactions, our model achieves better performance on predicting eye fixations in RGB-D images.
