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Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks.

Nian Liu, Junwei Han, Tianming Liu

IEEE Transactions on Neural Networks and Learning Systems

|December 4, 2016

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This study introduces a novel multiresolution convolutional neural network (Mr-CNN) to accurately predict human eye movements by integrating local contrast, global contrast, and top-down visual factors. The Mr-CNN model demonstrates superior performance in saliency detection for both natural and RGB-D images.

Area of Science:

Computer Vision
Computational Neuroscience
Machine Learning

Background:

Human eye movements during scene viewing are influenced by local contrast, global contrast, and top-down visual factors.
Existing models struggle with effectively modeling and integrating these saliency cues.

Purpose of the Study:

To propose a novel computational model for predicting eye fixations.
To effectively integrate local contrast, global contrast, and top-down visual factors using a unified framework.

Main Methods:

A multiresolution convolutional neural network (Mr-CNN) was developed to simultaneously infer saliency cues from raw image data.
The Mr-CNN was trained using fixation and nonfixation pixels with multiresolution inputs, learning to fuse contextual information for contrast cues and higher layers for top-down factors.

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The model was extended to RGB-D image saliency detection by jointly learning depth and RGB information.

Main Results:

The proposed Mr-CNN model significantly outperformed state-of-the-art methods on benchmark databases for eye fixation prediction.
The model demonstrated superior performance in saliency detection for RGB-D images by jointly processing depth and RGB information.

Conclusions:

The Mr-CNN provides a superior approach for modeling and integrating bottom-up and top-down saliency cues for eye fixation prediction.
The model's ability to jointly learn from RGB and depth information enhances its effectiveness for RGB-D image saliency detection.