Nonlinear data fusion in saliency-based visual attention

H. Bahmani, A. Nasrabadi, M. Gholpayeghani
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引用次数: 7

Abstract

Primates use saliency-based visual attention to detect conspicuous objects in cluttered visual environments. Some new strategies of combining feature maps to form a saliency map are addressed in this paper. Traditional methods of making saliency map are to linearly combine feature maps extracted from early visual system. Here we have proposed some modifications in saliency model with three different data fusion schemes: weighted linear combination of feature maps, multiplicative saliency map, and harmonic mean of feature maps. Experiments are based on a 32 images dataset of emergency triangle in natural environments. Comparison with the basic saliency model has also been provided. Results suggest that nonlinear combination of feature activities could perform a more accurate detection, and speeds up the process of finding a desired object in the scene.
基于显著性视觉注意的非线性数据融合
灵长类动物利用基于显著性的视觉注意在杂乱的视觉环境中发现显眼的物体。本文讨论了将特征映射组合成显著性映射的一些新策略。传统的显著性图制作方法是将早期视觉系统中提取的特征图进行线性组合。本文提出了三种不同的数据融合方案对显著性模型的改进:特征映射加权线性组合、乘法显著性映射和特征映射调和均值。实验基于自然环境下32幅应急三角图像数据集。并与基本显著性模型进行了比较。结果表明,特征活动的非线性组合可以实现更准确的检测,并加快在场景中找到所需目标的过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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