CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
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发表时间：
2015-05
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通讯作者：
A. Borji;L. Itti
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作者：
A. Borji;L. Itti
近二十年来，显著性建模一直是计算机视觉领域的一个活跃研究领域。现有的最先进的模型在预测人们在自然场景中看哪里方面表现得非常好。然而，存在这样的风险，即这些模型可能过度拟合了现有的小规模偏倚数据集，从而将进展困在局部最小值中。为了更深入地了解当前显著性建模的问题并更好地衡量进展，我们记录了120名观察者在自由观看大量自然和人工图像时的眼球运动。我们的刺激包括4000个图像；来自20个类别中的200个，涵盖不同类型的场景，如卡通，艺术，对象，低分辨率图像，室内，室外，杂乱，随机和线条图。我们分析了该数据集的一些基本属性，并比较了一些成功的模型。我们相信我们的数据集为下一代显著性模型开辟了新的挑战，并有助于进行自下而上视觉注意的行为研究。
Saliency modeling has been an active research area in computer vision for about two decades. Existing state of the art models perform very well in predicting where people look in natural scenes. There is, however, the risk that these models may have been overfitting themselves to available small scale biased datasets, thus trapping the progress in a local minimum. To gain a deeper insight regarding current issues in saliency modeling and to better gauge progress, we recorded eye movements of 120 observers while they freely viewed a large number of naturalistic and artificial images. Our stimuli includes 4000 images; 200 from each of 20 categories covering different types of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor, Outdoor, Jumbled, Random, and Line drawings. We analyze some basic properties of this dataset and compare some successful models. We believe that our dataset opens new challenges for the next generation of saliency models and helps conduct behavioral studies on bottom-up visual attention.