A proto-object-based computational model for visual saliency

Abstract

State-of-the-art bottom-up saliency models often assign high saliency values at or near high-contrast edges, whereas people tend to look within the regions delineated by those edges, namely the objects. To resolve this inconsistency, in this work we estimate saliency at the level of coherent image regions. According to object-based attention theory, the human brain groups similar pixels into coherent regions, which are called proto-objects. The saliency of these proto-objects is estimated and incorporated together. As usual, attention is given to the most salient image regions. In this paper we employ state-of-the-art computer vision techniques to implement a proto-object-based model for visual attention. Particularly, a hierarchical image segmentation algorithm is used to extract proto-objects. The two most powerful ways to estimate saliency, rarity-based and contrast-based saliency, are generalized to assess the saliency at the proto-object level. The rarity-based saliency assesses if the proto-object contains rare or outstanding details. The contrast-based saliency estimates how much the proto-object differs from the surroundings. However, not all image regions with high contrast to the surroundings attract human attention. We take this into account by distinguishing between external and internal contrast-based saliency. Where the external contrast-based saliency estimates the difference between the proto-object and the rest of the image, the internal contrast-based saliency estimates the complexity of the proto-object itself. We evaluate the performance of the proposed method and its components on two challenging eye-fixation datasets (Judd, Ehinger, Durand, & Torralba, 2009; Subramanian, Katti, Sebe, Kankanhalli, & Chua, 2010). The results show the importance of rarity-based and both external and internal contrast-based saliency in fixation prediction. Moreover, the comparison with state-of-the-art computational models for visual saliency demonstrates the advantage of proto-objects as units of analysis.