Global and graph encoded local discriminative region representation for scene recognition

Abstract

Scene recognition is a fundamental task in computer vision, which generally includes three vital stages, namely feature extraction, feature transformation and classification. Early research mainly focuses on feature extraction, but with the rise of Convolutional Neural Networks (CNNs), more and more feature transformation methods are proposed based on CNN features. In this work, a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation (GEDRR) is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions. In addition, we propose a method using the multi-head attention module to enhance and fuse convolutional feature maps. Combining the two methods and the global representation, a scene recognition framework called Global and Graph Encoded Local Discriminative Region Representation (G2ELDR2) is proposed. The experimental results on three scene datasets demonstrate the effectiveness of our model, which outperforms many state-of-the-arts.