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A new framework, termed spatially aligned pyramid matching, is proposed for near duplicate image identification. The proposed method robustly handles spatial shifts as well as scale changes, and is extensible for video data. Images are divided into both overlapped and non-overlapped blocks over multiple levels. In the first matching stage, pairwise distances between blocks from the examined image...
We introduce a generalized representation for a boosted classifier with multiple exit nodes, and propose a method to training which combines the idea of propagating scores across boosted classifiers [14, 17] and the use of asymmetric goals [13]. A means for determining the ideal constant asymmetric goal is provided, which is theoretically justified under a conservative bound on the ROC operating point...
Training a cascade-based face detector using boosting and Haar features is computationally expensive, often requiring weeks on single CPU machines. The bottleneck is at training and selecting Haar features for a single weak classifier, currently in minutes. Traditional techniques for training a weak classifier usually run in 0(NT log N), with N examples (approximately 10,000), and T features (approximately...
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