Nonnegative correlation coding for image classification

Feature coding is one of the most important procedures in the bag-of-features model for image classification. In this paper, we propose a novel feature coding method called nonnegative correlation coding. In order to obtain a discriminative image representation, our method employs two correlations: the correlation between features and visual words, and the correlation between the obtained codes. The first correlation reflects the locality of codes, i.e., the visual words close to the local feature are activated more easily than the ones distant. The second correlation characterizes the similarity of codes, and it means that similar local features are likely to have similar codes. Both correlations are modeled under the nonnegative constraint. Based on the Nesterov’s gradient projection algorithm, we develop an effective numerical solver to optimize the nonnegative correlation coding problem with guaranteed quadratic convergence. Comprehensive experimental results on publicly available datasets demonstrate the effectiveness of our method.创新点本文提出了一种用于图像分类的编码方法,称为“非负关联编码”。为了获得有判别力的图像表示,非负关联编码利用了两种关系:一是待编码的局部特征与视觉单词之间的关系,它反映了编码过程的局部性,即局部特征倾向于利用距离它较近的视觉单词进行表达;二是编码之间的关系,它体现了编码过程的相似性,即相似的局部特征具有相似的编码。这两种关系都在非负约束的条件下建模。另外,本文基于NGP(Nesterov梯度投影)方法提出了一种用于求解非负关联编码的有效算法。公共数据集上的实验结果证明了方法的有效性。