In this paper, we propose a biologically inspired model of the middle stages of attention, with specific algorithmic details. Exist- ing computational models of attention concentrate on their role in visual feature extraction and the selection of spatial regions. However, these methods ignore the role of attention in other stages. Extension of these models has been proposed by augmenting the unit of attentional selection to {"}proto-objects{"}. In our approach, we extend attention to the middle stages and integrate the selection process with the perceptual grouping process. Integration is achieved by our innovative saliency driven per- ceptual grouping strategy, extending the traditional pixel-based saliency map to salient proto-objects. The proposed selective attention is made in two stages. Firstly, to achieve salient region localization, our method enhances the saliency map with region information from image segmen- tation and selects the most salient region (proto-object). Then, regions are organized using perceptual groupings, and their pop-out sequence is determined. Compared with traditional attention models our model pro- vides saliency maps with meaningful region information, by eliminating misleading high-contrast edges, and focus of attention shifts in unit of perceptual object rather than spatial region. These two improvements ¯t to high stage vision information processing such as object recognition. Experiments in a reduced set of images show that our proposed model is able to automatically detect meaningful proto-objects.
Geerinck, T, Sahli, H, Henderickx, D, Vanhamel, I & Enescu, V 2009, Modeling Attention and Perceptual Grouping to Salient Objects. in L Paletta & E Rome (eds), Attention in Cognitive Systems, Lecture Notes In Artificial Intelligence; Vol. 5395. LNCS-LNAI, Springer Verlag, pp. 166-182.
Geerinck, T., Sahli, H., Henderickx, D., Vanhamel, I., & Enescu, V. (2009). Modeling Attention and Perceptual Grouping to Salient Objects. In L. Paletta, & E. Rome (Eds.), Attention in Cognitive Systems, Lecture Notes In Artificial Intelligence; Vol. 5395 (pp. 166-182). (LNCS-LNAI). Springer Verlag.
@inbook{ef8d52cb90924de1a8d8d83816331482,
title = "Modeling Attention and Perceptual Grouping to Salient Objects",
abstract = "In this paper, we propose a biologically inspired model of the middle stages of attention, with specific algorithmic details. Exist- ing computational models of attention concentrate on their role in visual feature extraction and the selection of spatial regions. However, these methods ignore the role of attention in other stages. Extension of these models has been proposed by augmenting the unit of attentional selection to {"}proto-objects{"}. In our approach, we extend attention to the middle stages and integrate the selection process with the perceptual grouping process. Integration is achieved by our innovative saliency driven per- ceptual grouping strategy, extending the traditional pixel-based saliency map to salient proto-objects. The proposed selective attention is made in two stages. Firstly, to achieve salient region localization, our method enhances the saliency map with region information from image segmen- tation and selects the most salient region (proto-object). Then, regions are organized using perceptual groupings, and their pop-out sequence is determined. Compared with traditional attention models our model pro- vides saliency maps with meaningful region information, by eliminating misleading high-contrast edges, and focus of attention shifts in unit of perceptual object rather than spatial region. These two improvements ¯t to high stage vision information processing such as object recognition. Experiments in a reduced set of images show that our proposed model is able to automatically detect meaningful proto-objects.",
keywords = "Visual attention mechanism, Perceptual Grouping, Salient object of interest",
author = "Thomas Geerinck and Hichem Sahli and David Henderickx and Iris Vanhamel and Valentin Enescu",
note = "L. Paletta and E. Rome",
year = "2009",
language = "English",
isbn = "978-3-642-00581-7",
series = "LNCS-LNAI",
publisher = "Springer Verlag",
pages = "166--182",
editor = "L. Paletta and E. Rome",
booktitle = "Attention in Cognitive Systems, Lecture Notes In Artificial Intelligence; Vol. 5395",
address = "Germany",
}