Image set-based classification using collaborative exemplars representation

In many classification tasks, multiple images that form image set may be available rather than a single image for object. For image set classification, crucial issues include how to simply and efficiently represent the image sets and deal with outliers. In this paper, we develop a novel method, called image set-based classification using collaborative exemplars representation, which can achieve the data compression by finding exemplars that have a clear physical meaning and remove the outliers that will significantly degrade the classification performance. Specifically, for each gallery set, we explicitly select its exemplars that can appropriately describe this image set. For probe set, we can represent it collaboratively over all the gallery sets formed by exemplars. The distance between the query set and each gallery set can then be evaluated for classification after resolving representation coefficients. Experimental results show that our method outperforms the state-of-the-art methods on three public face datasets, while for object classification, our result is very close to the best result.