  With the rapid development of machine vision technology in recent years, many
researchers have begun to focus on feature compression that is better suited
for machine vision tasks. The target of feature compression is deep features,
which arise from convolution in the middle layer of a pre-trained convolutional
neural network. However, due to the large volume of data and high level of
abstraction of deep features, their application is primarily limited to
machine-centric scenarios, which poses significant constraints in situations
requiring human-computer interaction. This paper investigates features and
textures and proposes a texture-guided feature compression strategy based on
their characteristics. Specifically, the strategy comprises feature layers and
texture layers. The feature layers serve the machine, including a feature
selection module and a feature reconstruction network. With the assistance of
texture images, they selectively compress and transmit channels relevant to
visual tasks, reducing feature data while providing high-quality features for
the machine. The texture layers primarily serve humans and consist of an image
reconstruction network. This image reconstruction network leverages features
and texture images to reconstruct preview images for humans. Our method fully
exploits the characteristics of texture and features. It eliminates feature
redundancy, reconstructs high-quality preview images for humans, and supports
decision-making. The experimental results demonstrate excellent performance
when employing our proposed method to compress the deep features.
