Cross-domain concept detection with dictionary coherence by leveraging web images

Abstract

We propose a novel scheme to address video concept learning by leveraging social media, one that includes the selection of web training data and the transfer of subspace learning within a unified framework. Due to the existence of cross-domain incoherence resulting from the mismatch of data distributions, how to select sufficient positive training samples from scattered and diffused social media resources is a challenging problem in the training of effective concept detectors. In this paper, given a concept, the coherent positive samples from web images for further concept learning are selected based on the degree of image coherence. Then, by exploiting both the selected dataset and video keyframes, we train a robust concept classifier by means of a transfer subspace learning method. Experiment results demonstrate that the proposed approach can achieve constant overall improvement despite cross-domain incoherence.