This image dataset which is also a mouse tracking dataset, has been created from a subset of images from a parent dataset called MS COCO 2014 (available on http://cocodataset.org/#home) with an additional annotation type “fixation”. The visual attentional data for this dataset is collected by using mouse tracking methods. The research work related to this dataset aimed to find answers about human visual attention and decision making. In their paper which is available in bellow, they evaluated their mouse tracking method by comparing the results with eye-tracking.
Here is some information regarding the latest version of this dataset:
Number of images in the dataset: 20,000 (10,000 images for training set, 5000 images for validation, 5000 for test set)
Number of classes: 80
Image resolution: 640×480
More details and links for download can be found on the dataset page http://salicon.net/ and SALICON challenge 2017 page http://salicon.net/challenge-2017/.
You might also be interested to use the SALICON API Python package available on GitHub https://github.com/NUS-VIP/salicon-api.
If you use this dataset:
Please make sure to cite the paper:
M. Jiang, S. Huang, J. Duan, Q. Zhao, SALICON: Saliency in Context. CVPR 2015.
keywords: Vision, Image, Classification, Scene, Saliency Analysis