Combining CNN with Hand-Crafted Features for Image Classification

Abstract

Convolutional neural networks (CNN) have achieved outstanding performance in image recognition tasks, but many hand-crafted features still play important roles in some areas. Hand-crafted features are designed to describe image content from specific aspects, which may provide complementary information for CNN in image classification tasks. This paper explores feature fusion methods and proposes a novel framework for combining CNN with hand-crafted features. The framework has two main advantages. First, feature encoder can encode non-normalized features in CNN, which takes advantage of some good edge, texture and local features. Second, joint training strategy makes features fuse better in CNN. We validate that many handcrafted features help to improve the performance of origin CNN. Experiments show our method outperforms the origin CaffeNet on Cifar10 dataset with 79.16% accuracy.