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A Benchmark for Region-of-Interest Detection in Images
Date Issued
2007
Date
2007
Author(s)
Huang, Tz-Huan
DOI
en-US
Abstract
This thesis presents a benchmark for region of interest (ROI) detection. ROI detection has many useful applications and many algorithms have been proposed to automatically detect ROIs. Unfortunately, due to the lack of benchmarks, these methods were often tested on small data sets that are not available to others, making fair comparisons of these methods difficult. Examples from many fields have shown that repeatable experiments using published benchmarks are crucial to the fast advancement of the fields. To fill the gap, this thesis presents our design for a collaborative game, called Photoshoot, to collect human ROI annotations for constructing an ROI benchmark. With this game, we have gathered a large number of annotations and fused them into aggregated ROI models. We use these models to evaluate five ROI detection algorithms quantitatively. Furthermore, by using the benchmark as training data, learning-based ROI detection algorithms become viable.
Subjects
使用者感興趣區域,資料集,遊戲
region of interest,benchmark,game
Type
thesis
File(s)
No Thumbnail Available
Name
ntu-96-R94922044-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):c85bb139c601eeb93f98a9b0ad2624e7