计算机与现代化 ›› 2011, Vol. 1 ›› Issue (3): 21-24.doi: 10.3969/j.issn.1006-2475.2011.03.007

• 人工智能 • 上一篇    下一篇

视觉显著性参数调整

李 荣1,约克•康哈特2,朱芳来1   

  1. 1.同济大学电子与信息工程学院,上海 201804;2.慕尼黑工业大学电气工程与信息技术学院,德国 慕尼黑 80333
  • 收稿日期:2011-01-13 修回日期:1900-01-01 出版日期:2011-03-18 发布日期:2011-03-18

Visual Saliency Parameter Tuning

LI Rong1, Jorg CONRADT2, ZHU Fang-lai1   

  1. 1. School of Electronics and Information, Tongji University, Shanghai 201804, China;2. Department of Electrical Engineering and Information Technology, Technical University of Munich, Munich 80333, Germany
  • Received:2011-01-13 Revised:1900-01-01 Online:2011-03-18 Published:2011-03-18

摘要: 基于显著性的视觉注意力模型近年来得到广泛应用。基于显著性的视觉注意将复杂的景物理解问题转化成一种局部模式识别的时间层序问题,具有计算高效、实时的特性。本文基于Itti视觉注意力模型,评估特定参数对显著图性能的影响。运用统计学的方法,获得显著图的最优参数,用于改善显著图的性能。理论分析和实验结果表明,该方法能更快速、准确地发现目标,减少目标的截获时间,提高目标的跟踪性能。

关键词: 显著图, 参数调整, 视觉注意

Abstract: Saliency-based visual attention model is widely used in recent years. Saliency-based visual attention allows for seemingly real-time performance by breaking down the complexity of scene understanding into a fast temporal sequence of localized pattern recognition problems, in a computationally efficient manner. Based on Itti’s visual attention model, this paper evaluates the effects of specific parameters on the performance of saliency map. Statistical methods are applied to choose optimal parameters, which allowed to improve the performance of saliency map. Some experimental results show that the method can quickly and accurately detect targets in a complex background.

Key words: saliency map, parameter tuning, visual attention

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