Abstract
This paper presents a large scale general purpose image database with human annotated ground truth. Firstly, an all-in-all labeling framework is proposed to group visual knowledge of three levels: scene level (global geometric description), object level (segmentation, sketch representation, hierarchical decomposition), and low-mid level (2.1D layered representation, object boundary attributes, curve completion, etc.). Much of this data has not appeared in previous databases. In addition, And-Or Graph is used to organize visual elements to facilitate top-down labeling. An annotation tool is developed to realize and integrate all tasks. With this tool, we’ve been able to create a database consisting of more than 636,748 annotated images and video frames. Lastly, the data is organized into 13 common subsets to serve as benchmarks for diverse evaluation endeavors.
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Yao, B., Yang, X., Zhu, SC. (2007). Introduction to a Large-Scale General Purpose Ground Truth Database: Methodology, Annotation Tool and Benchmarks. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_14
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DOI: https://doi.org/10.1007/978-3-540-74198-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74195-4
Online ISBN: 978-3-540-74198-5
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