Efficient instance search from large video database via sparse filters in subspaces

In this paper, we propose a biologically inspired approach to overcome the challenges of searching instances from large video databases. Specifically, we train sparse filters in subspaces from unlabelled natural images, then yield image feature for new image instances through pre-learned filters. Therefore, no traditional “hand-designed” features (e.g. colour histograms, interest point descriptors) are required in our system. Experiments on a challenging large video database containing 20982 videos show our approach outperforms traditional approaches such as Bag-of-Words using SURF, or the combination of SIFT, SURF, RGB and texture features.