Issue No. 03 - March (2011 vol. 33)
Alexandre Karpenko , University of Toronto, Toronto
Parham Aarabi , University of Toronto, Toronto
In this paper, we present a large database of over 50,000 user-labeled videos collected from YouTube. We develop a compact representation called “tiny videos” that achieves high video compression rates while retaining the overall visual appearance of the video as it varies over time. We show that frame sampling using affinity propagation—an exemplar-based clustering algorithm—achieves the best trade-off between compression and video recall. We use this large collection of user-labeled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The classification results achieved by tiny videos are compared with the tiny images framework  for a variety of recognition tasks. The tiny images data set consists of 80 million images collected from the Internet. These are the largest labeled research data sets of videos and images available to date. We show that tiny videos are better suited for classifying scenery and sports activities, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos data sets improves classification precision in a wider range of categories.
Image classification, content-based retrieval, tiny videos, tiny images, data mining, nearest-neighbor methods.
A. Karpenko and P. Aarabi, "Tiny Videos: A Large Data Set for Nonparametric Video Retrieval and Frame Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 618-630, 2010.