Cosine Activation in Compact Network (CACN): Application to Scene Classification

In this paper, we propose a new learning architecture named cosine activation in a compact network (CACN). The CACN is derived from kernel approximation and establishes a nonlinear hidden layer with the cosine activation function. By inheriting fusion ability in kernel approximation while learning parameters in a supervised way, the CACN is a well-directed solution to scene classification. By seamlessly connecting with convolutional neural networks (CNNs), it is easy to construct an end-to-end network. To compensate for the loss of spatial layout information in CNNs, the CACN is further combined with spatial pyramid matching to fuse various information into one holistic picture. The experiments on the MIT indoor and SUN397 datasets show that the CACN delivers high performance and demonstrates its great effectiveness for scene-classification tasks.