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基于E2LSH-MKL的视觉语义概念检测

DOI: 10.3724/SP.J.1004.2012.01671, PP. 1671-1678

Keywords: 视觉语义概念,多核学习,精确欧氏空间位置敏感哈希算法,Hadamard内积

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Abstract:

?多核学习方法(Multiplekernellearning,MKL)在视觉语义概念检测中有广泛应用,但传统多核学习大都采用线性平稳的核组合方式而无法准确刻画复杂的数据分布.本文将精确欧氏空间位置敏感哈希(ExactEuclideanlocalitysensitiveHashing,E2LSH)算法用于聚类,结合非线性多核组合方法的优势,提出一种非线性非平稳的多核组合方法—E2LSH-MKL.该方法利用Hadamard内积实现对不同核函数的非线性加权,充分利用了不同核函数之间交互得到的信息;同时利用基于E2LSH哈希原理的聚类算法,先将原始图像数据集哈希聚类为若干图像子集,再根据不同核函数对各图像子集的相对贡献大小赋予各自不同的核权重,从而实现多核的非平稳加权以提高学习器性能;最后,把E2LSH-MKL应用于视觉语义概念检测.在Caltech-256和TRECVID2005数据集上的实验结果表明,新方法性能优于现有的几种多核学习方法.

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