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A Computational Model for Object-Based Visual Saliency: Spreading Attention Along Gestalt Cues

机译：基于对象的视觉显着性的计算模型：沿着格式塔提示传播注意力

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摘要

The past few years have witnessed impressive progress on the research of salient object detection. Nevertheless , existing approaches still cannot perform satisfactorily in the case of complex scenes, particularly when the salient objects have non- uniform appearance or complicated shapes, and the background is complexly structured. One important reason for such limitations may be that these approaches commonly ignore the factor of perceptual grouping in saliency modeling. To address this issue, this paper presents a novel computational model for object -based visual saliency, which explicitly takes into consideration the connections between attention and perceptual grouping, and incorporates Gestalt grouping cues into saliency computation. Inspired by the sensory enhancement theory, we suggest a paradigm for object-based saliency modeling, that is, object-based saliency stems from spreading attention along Gestalt grouping cues. Computationally , three typical Gestalt cues, including proximity, similarity, and closure, are respectively extracted from the given image, which are then integrated by constructing a unified Gestalt graph. A new algorithm named personalized power iteration clustering is developed to effectively fulfill the spreading of attention information across the Gestalt graph. Intensive experiments have been carried out to demonstrate the superior performance of the proposed model in comparison to the state-of-the-art.
机译：过去几年中，显着物体检测的研究取得了令人瞩目的进展。然而，在复杂场景的情况下，特别是当显着物体的外观不均匀或形状复杂且背景结构复杂时，现有方法仍然不能令人满意地执行。这种局限性的一个重要原因可能是这些方法在显着性建模中通常忽略了感知分组的因素。为了解决这个问题，本文提出了一种新颖的基于对象的视觉显着性计算模型，该模型明确考虑了注意力和感知分组之间的联系，并将格式塔分组线索纳入显着性计算中。受感官增强理论的启发，我们提出了基于对象的显着性建模的范式，即基于对象的显着性源于沿格式塔分组线索传播注意力。在计算上，分别从给定图像中提取出三个典型的格式塔线索，包括邻近度，相似度和闭合度，然后通过构建统一的格式塔图将其整合。开发了一种名为个性化功率迭代聚类的新算法，以有效地实现在完形图上分散注意力信息。与现有技术相比，已经进行了密集的实验以证明所提出模型的优越性能。

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