利用编码层特征组合进行场景分类

Scene classification based on coding layer feature combination

为了提高图像场景分类效果,一种有效的方法是将不同特征组合起来,以常用的SIFT特征和归一化颜色直方图(NCH)特征为例研究了不同语义层次上的组合——特征层的组合和编码层的组合以及它们的效果。在几个常用数据库上的实验结果表明,相比简单的组合特征,在特征提取后再进行组合能在降低特征维数的情况下保持分类的效果,而在编码层的特征组合能获得更高的分类准确率。这表明特征组合应尽量在较高的语义层次上进行。

In order to improve classification performance of image,one efficient method was to combine different features together.The combination in various layers(feature layer and coding layer) was studied with the help of SIFT feature and normalized color histogram(NCH) feature,their effects were also compared.Experiments on several popular datasets show that,compared to combined features,the feature combination after feature extraction can reduce feature dimension while keep the similar classification rate,and the feature combination on coding layer can achieve even higher classification rate.It is concluded that feature combination should be better performed on higher semantic level.