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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/17690

Title: 50,000 Tiny Videos: A Large Dataset for Non-parametric Content-based Retrieval and Recognition
Authors: Karpenko, Alexandre
Advisor: Aarabi, Parham
Department: Electrical and Computer Engineering
Keywords: computer vision
video retrieval
image recognition
Issue Date: 22-Sep-2009
Abstract: This work extends the tiny image data-mining techniques developed by Torralba et al. to videos. A large dataset of over 50,000 videos was collected from YouTube. This is the largest user-labeled research database of videos available to date. We demonstrate that a large dataset of tiny videos achieves high classification precision in a variety of content-based retrieval and recognition tasks using very simple similarity metrics. Content-based copy detection (CBCD) is evaluated on a standardized dataset, and the results are applied to related video retrieval within tiny videos. We use our similarity metrics to improve text-only video retrieval results. Finally, we apply our large labeled video dataset to various classification tasks. We show that tiny videos are better suited for classifying activities than tiny images. Furthermore, we demonstrate that classification can be improved by combining the tiny images and tiny videos datasets.
URI: http://hdl.handle.net/1807/17690
Appears in Collections:Master
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses

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