Few-shot learning aims to learn classification with only a few training examples per class. The metric-based approaches aim to learn a set of embedding functions, so that when represented in this embedding, images are easy to recognize. For metric-based few-shot learning, how to get the class feature representation under a few support samples and what metric to use are important. We propose a multiclass triplet metric-learning network combined with a simple foreground–background feature mixing block. With the foreground–background feature mixing block, we “hallucinate” the information from few support examples to get conceptual representation of classes, which is effective to promote few-shot learning. Furthermore, using the multiclass triplet loss, it learns a feature embedding function that could bring similar samples close to each other and keep samples of different classes far apart. Our proposed network is trained in an end-to-end manner from scratch, so as to learn a good embedding function, conceptual representation of classes, and a nonlinear metric simultaneously. Experimental results on the challenging datasets show that our method with Conv-64F feature extracting block is competitive and effective compared to the metric-based baselines with Conv-64F.