基于颜色的压缩层次图像表示方法

空间金字塔模型在每层中把图像划分成细胞单元用于给图像表示提供空间信息，但是这种方式不能很好地匹配对象上的不同部分，为此提出一种基于颜色的层次（CL）划分算法。CL算法从多特征融合的角度出发，通过优化的方式在不同层次中得到每个类别中有判别力的颜色，然后根据每层中有判别力的颜色对图像进行迭代的层次划分；最后连接不同层次直方图用于图像表示。为了解决图像表示维度过高的问题，采用分化信息理论的特征聚类（DITC）方法对字典进行聚类用于字典降维，并用压缩生成的字典进行最终的图像表示。实验结果表明，所提方法能够在Soccer，Flower 17和Flower 102上取得良好的识别效果。

Color based compact hierarchical image representation

The spatial pyramid matching method provides the spatial information by splitting an image into different cells. However, spatial pyramid matching can not match different parts of the objects well. A hierarchical image representation method based on Color Level (CL) was proposed. The class-specific discriminative colors of different levels were obtained from the viewpoint of feature fusion in CL algorithm, and then an image was iteratively split into different levels based on these discriminative colors. Finally, image representation was constructed by concatenating the histograms of different levels. To reduce the dimensionality of image representation, the Divisive Information-Theoretic feature Clustering (DITC) method was used to cluster the dictionary, and the generated compact dictionary was used for final image representation. Classification results on Soccer, Flower 17 and Flower 102 datasets, demonstrate that the proposed method can obtain satisfactory results in these datasets.