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Proto-Object Based Saliency Model With Texture Detection Channel
Frontiers in Computational Neuroscience ( IF 3.2 ) Pub Date : 2020-09-24 , DOI: 10.3389/fncom.2020.541581
Takeshi Uejima 1 , Ernst Niebur 2 , Ralph Etienne-Cummings 1
Affiliation  

The amount of visual information projected from the retina to the brain exceeds the information processing capacity of the latter. Attention, therefore, functions as a filter to highlight important information at multiple stages of the visual pathway that requires further and more detailed analysis. Among other functions, this determines where to fixate since only the fovea allows for high resolution imaging. Visual saliency modeling, i.e. understanding how the brain selects important information to analyze further and to determine where to fixate next, is an important research topic in computational neuroscience and computer vision. Most existing bottom-up saliency models use low-level features such as intensity and color, while some models employ high-level features, like faces. However, little consideration has been given to mid-level features, such as texture, for visual saliency models. In this paper, we extend a biologically plausible proto-object based saliency model by adding simple texture channels which employ nonlinear operations that mimic the processing performed by primate visual cortex. The extended model shows statistically significant improved performance in predicting human fixations compared to the previous model. Comparing the performance of our model with others on publicly available benchmarking datasets, we find that our biologically plausible model matches the performance of other models, even though those were designed entirely for maximal performance with little regard to biological realism.

中文翻译:

具有纹理检测通道的基于原型对象的显着性模型

从视网膜投射到大脑的视觉信息量超过了后者的信息处理能力。因此,注意力起到过滤器的作用,突出显示视觉通路多个阶段的重要信息,这些信息需要进一步、更详细的分析。除其他功能外,这还决定了注视位置,因为只有中央凹才允许高分辨率成像。视觉显着性建模,即了解大脑如何选择重要信息进行进一步分析并确定下一步关注的位置,是计算神经科学和计算机视觉中的一个重要研究课题。大多数现有的自下而上的显着性模型使用强度和颜色等低级特征,而某些模型则采用面部等高级特征。然而,很少考虑视觉显着性模型的中级特征,例如纹理。在本文中,我们通过添加简单的纹理通道来扩展生物学上合理的基于原型对象的显着性模型,该纹理通道采用模仿灵长类视觉皮层执行的处理的非线性操作。与之前的模型相比,扩展模型在预测人类注视点方面显示出统计上显着的性能改进。将我们的模型与公开可用的基准数据集上的其他模型的性能进行比较,我们发现我们的生物学合理模型与其他模型的性能相匹配,即使这些模型完全是为了最大性能而设计的,而很少考虑生物现实性。
更新日期:2020-09-24
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