Huang Ke, Aviyente Selin
Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA.
IEEE Trans Image Process. 2008 Sep;17(9):1709-20. doi: 10.1109/TIP.2008.2001050.
Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.
小波子带上的能量分布是基于小波包的纹理分类中广泛使用的一种特征。由于小波包分解的过完备特性，通常会应用特征选择来获得更好的分类精度和紧凑的特征表示。大多数小波特征选择算法分别基于对每个子带的评估来进行特征选择，这隐含地假设来自不同子带的小波特征是独立的。本文针对给定的图像模型，从理论上研究并模拟了不同子带特征之间的相关性。基于分析和模拟，提出了一种基于统计相关性的小波特征选择算法。通过结合小波特征之间的相关性和对单个特征分量的评估，对该算法进行了进一步改进。实验结果表明了所提算法在将相关性纳入小波特征选择中的有效性。
