DSSESKN: A depthwise separable squeeze-and-excitation selective kernel network for art image classification

Abstract

Image classification is one of the key technologies of content-based image retrieval, and it is also the focus and hotspot of image content analysis research in recent years. Through the image processing and analysis technology to automatically analyze the image content to complete the management and retrieval of images, this process is the main content for image classification. Faced with massive digital Chinese art works, how to achieve effective management and retrieval for them has become an urgent problem to be solved. Traditional image retrieval technology is mainly based on image annotation, which has many problems, such as large workload and not objective enough. In this paper, we propose a depthwise separable squeeze-and-excitation selective kernel network (DSSESKN) for art image classification. SKNet (Slective Kernel Network) is used to adaptively adjust the receptive field to extract the global and detailed features of the image. We use SENet (squeeze-and-excitation network) to enhance the channel features. SKNet and SENet are fused to built the DSSESKN. The convolution kernel on the branch of DSSESKN module is used to extract the global feature and local detail features of the input image. The feature maps on the branches are fused, and the fused feature maps are compressed and activated. The processed feature weights are mapped to the feature maps of different branches and feature fusion is carried out. Art images are classified by deep separable convolution. Finally, we conduct experiments with other state-of-the-art classification methods, the results show that the effectiveness of the DSSESKN obtains the better effect.