论文笔记之:Multiple Feature Fusion via Weighted Entropy for Visual Tracking
2016-09-27 16:41
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Multiple Feature Fusion via Weighted Entropy for Visual Tracking
ICCV 2015
本文主要考虑的是一个多特征融合的问题.如何有效的进行加权融合,是一个需要解决的问题.本文提出一种新的 data-adaptive visual tracking approach 通过 weighted entropy 进行多特征融合.并非像许多方法所做的简单的链接在一起的方法,本文采用加权的 entropy 来评价目标状态和背景状态之间的区分性,寻求最优的特征组合方案,所以可以充分的利用信息互补的特征进行物体的表示.实验表明本文方法在跟踪领域的有效性.
引言:
ICCV 2015
本文主要考虑的是一个多特征融合的问题.如何有效的进行加权融合,是一个需要解决的问题.本文提出一种新的 data-adaptive visual tracking approach 通过 weighted entropy 进行多特征融合.并非像许多方法所做的简单的链接在一起的方法,本文采用加权的 entropy 来评价目标状态和背景状态之间的区分性,寻求最优的特征组合方案,所以可以充分的利用信息互补的特征进行物体的表示.实验表明本文方法在跟踪领域的有效性.
引言:
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