Kernel pooling feature representation of pre-trained convolutional neural networks for leaf recognition

Due to the presence of various types of factors, such as illumination, viewpoint, intra-class complexity, and inter-class similarity, which make plant leaf recognition still a challenging research problem. In this paper, we present a very simple, yet effective feature representation method for plant leaf recognition. Concretely, it comprises four stages. Firstly, each leaf image is fed into an imagenet pre-trained CNN model to extract activated feature maps in a specified layer. Secondly, inspired by 1 x1 convolution, we exploit principle component analysis to learn the 1 x1 convolution filters. As a result, it not only eliminates the redundant information, reduces the feature dimension adaptively that is beneficial to the subsequent high order pooling, but also increases classification accuracy. Thirdly, kernel pooling is employed to capture second order statistics between each pair of features with the purpose of learning more discriminative information. Finally, matrix sqrt and upper triangle are performed to obtain the final leaf representation, which is utilized for classification and retrieval by the euclidean distance based nearest neighbor classifier. Extensive experiments are conducted on four representative plant leaf datasets, Flavia, Swedish, MEW2012, ICL, to validate the effectiveness of our method. For classification task, our method achieves outstanding and better average classification accuracies than the comparative state-of-theart baselines. For retrieval task, our method gets significant higher or competitive MAP scores. Our implementation code will be available at https://github.com/fengshu666666/leafrecognition.