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Saliency modeling has been an active research area in computer vision forabout two decades. Existing state of the art models perform very well inpredicting where people look in natural scenes. There is, however, the riskthat these models may have been overfitting themselves to available small scalebiased datasets, thus trapping the progress in a local minimum. To gain adeeper insight regarding current issues in saliency modeling and to bettergauge progress, we recorded eye movements of 120 observers while they freelyviewed a large number of naturalistic and artificial images. Our stimuliincludes 4000 images; 200 from each of 20 categories covering different typesof scenes such as Cartoons, Art, Objects, Low resolution images, Indoor,Outdoor, Jumbled, Random, and Line drawings. We analyze some basic propertiesof this dataset and compare some successful models. We believe that our datasetopens new challenges for the next generation of saliency models and helpsconduct behavioral studies on bottom-up visual attention.

CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research