Activated hidden units in convolutional neural networks (CNNs), known as feature maps, dominate image representation, which is compact and discriminative. For ultra-large datasets, high dimensional feature maps in float format not only result in high computational complexity, but also occupy massive memory space. To this end, a new image representation by aggregating convolution kernels (ACK) is proposed, where some convolution kernels capturing certain patterns are activated. The top-n index numbers of the convolution kernels are extracted directly as image representation in discrete integer values, which rebuild relationship between convolution kernels and image. Furthermore, a distance measurement is defined from the perspective of ordered sets to calculate position-sensitive similarities between image representations. Extensive experiments conducted on Oxford Buildings, Paris, and Holidays, etc., manifest that the proposed ACK achieves competitive performance on image retrieval with much lower computational cost, outperforming the ones using feature maps for image representation.

卷积神经网络（CNN）中的激活隐藏单元（称为特征图）在图像表示中占主导地位，该结构紧凑且具有区分性。对于超大型数据集，float格式的高维特征图不仅会导致较高的计算复杂度，而且还会占用大量内存空间。为此，提出了一种通过聚合卷积核（ACK）的新图像表示，其中激活了捕获某些模式的一些卷积核。卷积核的前n个索引号直接作为离散整数值中的图像表示而提取，从而重建了卷积核与图像之间的关系。此外，从有序集合的角度定义了距离测量，以计算图像表示之间的位置敏感相似度。