An Modified Earth Mover’s Distance for Few-Shot Image Classification

The problem of few-shot image classification has become a popular research area in the field of imaging. In order to improve the accuracy of few-shot image classification, many learning methods have been proposed, among which there are four main approaches: meta-based learning, data augmentation-based, migration-based learning and metric-based learning. In this paper, we propose a modified earth mover’s distance (MEMD) based on the metric learning approach, which has received much attention due to its simple structure and accurate classification. Similarly, MEMD constructs correlations between image regions, using such correlations to characterise the class of the image. MEMD generates a stream of best matches between image regions, and this stream of matches represents the similarity of the classified images. the MEMD algorithm requires the generation of feature weights for image regions, and EMD uses a mechanism of cross-referenced citations to generate uniform consultation weights. in contrast to EMD, MEMD generates inconsistent reference weights based on the similarity of regions. In dealing with the K-Shot problem, we used a learnable class prototype to characterise the class feature vectors. We conducted comprehensive experiments to validate our improved MEMD algorithm and tested it on four popular few-shot datasets. Namely: miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100) and the CUB dataset.