摘要： 局部Gist 方法提取的特征维数过高、计算复杂,单一的Gist 特征不能很好地描述全局场景。为此,提出一种将改进的局部Gist 特征与梯度方向直方图特征进行组合的场景描述方法。采用支持向量机作为分类器,在WS场景库中考察单一特征和组合特征的分类精度,在OT 场景库下研究不同数量训练样本对于分类精度的影响。实验结果表明,与全局Gist、局部Gist 等方法相比,该方法能降低计算的复杂度,且提高分类正确率。

关键词: 局部Gist 特征, 梯度方向直方图, 特征组合, 场景描述, 支持向量机, 场景分类

Abstract: In view of complex computation caused by extracting high dimension characteristics with local Gist method, as well as the problem that the sole Gist characteristic can not describe global scenes well,a kind of improved method to describe the scenes is proposed,which combines local Gist characteristics with Histograms of Oriented Gradient(HOG) characteristics. Classification accuracy of the sole characteristics and the combination of characteristics are inspected in the WS scene database using Support Vector Machine(SVM) as the classifier. On this basis,classified precision influenced by different quantity training samples is also studied in the OT scenes database. Experimental results show that this method reduces the computational complexity,and improves the classified accuracy compared with the global Gist,local Gist methods,etc.

Key words: local Gist feature, Histograms of Oriented Gradient(HOG), feature combination, scene description, Support Vector Machine(SVM), scene classification

中图分类号: 

• TP391. 4