Multiple Kernel Based Transfer Learning for the Few-Shot Recognition Task in Smart Home Scene

Abstract

With the development of artificial intelligence, smart home plays an increasingly important role in daily life. Since new objects may constantly appear at home and the collecting of enough training samples sometimes can be hard, the few-shot recognition task is essential and practical in smart home scene. MULticlass Transfer Incremental LEarning (MULTIpLE) is an effective algorithm that can perform transfer learning for class increment with few new samples based on Support Vector Machine (SVM). But the features of images are generally high-dimensional and the selection for kernel function affects the performance of MULTIpLE. In this paper, a new transfer learning algorithm based on multiple kernel learning, termed MULticlass Transfer Incremental LEarning based on Multiple Kernel Learning (MULTIpLE-MKL), is proposed for the few-shot recognition task in smart home scene. There are two main steps for the MULTIpLE-MKL, including the first multiple kernel learning stage and the second transfer learning stage. Specifically, multiple kernel learning is first applied in the construction of SVM models to optimize the selection of kernel function. When different kernels are calculated based on different features, the sparse kernel coefficients achieve the key feature selection. Second, the SVM models learn a new class from few samples by the virtue of the transfer learning algorithm, MULTIpLE. Compared with conventional methods, experiments based on the benchmark Caltech-256 dataset demonstrate that the proposed MULTIpLE-MKL not only maintains the good performance in original classes but also shows improving recognition ability for new classes only with few training samples.