Scene Recognition via Object-to-Scene Class Conversion: End-to-End Training

Hongje Seong, Junhyuk Hyun, Hyunbae Chang, Suhyeon Lee, Suhan Woo, Euntai Kim

Abstract: When a person recognize the scene of an image, contextual understanding from its environmental elements is necessary. These environmental elements are variant and require comprehensive understanding of various situations. Especially, objects are frequently used as environmental elements related with scene. In this paper, we suggest a score level Class Conversion Matrix (CCM) for scene recognition with a great focus on relationship between objects and scene. A lot of existing methods have already build scene recognition systems with consideration of close relationship between object and scenes. However, most of these methods are using the object features directly without any conversions or reconstructions, and it lack confirmation whether these object features are helpful to recognize scenes correctly. To solve this problem, CCM, a matrix converting object feature to scene feature, is suggested. Moreover, CCM can be implemented with neural network layer and end-to-end trainable. Extensive experiments on Places 2 dataset demonstrate the effectiveness of our approach, when it is applied to the existing deep convolutional neural network architectures. The code is available at https://github.com/Hongje/Class_Conversion_Matrix-Places365