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2009

SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images

8 years 9 months ago
SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images
Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose four simple yet powerful hybrid ROI detection methods (combining both local and global features), based on frequently occurring keypoints. We show that our methods demonstrate competitive performance in two different types of datasets, the Caltech101 dataset and the GRAZ-02 dataset, where the pairs of keypoint bounding box method achieved the best accuracies overall. Keywords-Image Processing; SIFT Keypoints; Image Recognition and Categorization; ROI Detection
Edmond Zhang, Michael Mayo
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2009
Where DICTA
Authors Edmond Zhang, Michael Mayo
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