A novel feature representation: Aggregating convolution kernels for image retrieval. (October 2020) - Wang, Qi
Abstract: 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. Highlights: Proposed a new image representation method based on convolution kernels index. Convolution kernel is equivalent to a feature extractor, which can be as descriptors directly. Explored a similarity measurement for new representation based on position-sensitive. Extended a new research area about image sequence representation.