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Computational Models of Shape Saliency

Patrik Polatsek, Marek Jakab, Wanda Benesova, Matej Kužma

Abstract. Computational models predicting stimulus-driven human visual attention usually incorporate simple visual features, such as intensity, color and orientation. However, saliency of shapes and their contour segments influence attention too. Therefore, we built 30 own shape saliency models based on existing shape representation and matching techniques and compared them with 5 existing saliency methods. Since available fixation datasets were usually recorded on natural scenes where various factors of attention are present, we performed a novel eye-tracking experiment that primarily focuses on shape and contour saliency. Fixations from 47 participants who looked at silhouettes of abstract and realworld objects were used to evaluate the accuracy of proposed saliency models and investigate which shape properties are most attentive. The results showed that visual attention integrates local contour saliency, saliency of global shape features and shape dissimilarities. Fixation data also showed that intensity and orientation contrasts play an important role in shape perception. We found that humans tend to fixate first irregular geometrical shapes and objects whose similarity to a circle is different from other objects.

shapeSal dataset contains an extended version of this eye-tracking experiment including images and fixation data (73 participants, 158 scenes).

download: [V2.0; update: 25.3.2019]

Please cite this paper if you use the dataset:

Polatsek, P., Jakab, M., Benesova, W., & Kužma, M. (2019)
Computational Models of Shape Saliency
11th International Conference on Machine Vision (ICMV 2018) (Vol. 11041)
International Society for Optics and Photonics