Improving Efficiency and Accuracy for SIFT Streetscape Recognition System
Date Issued
2012
Date
2012
Author(s)
Lu, Cheng-Hung
Abstract
This research presents a novel streetscape recognition system which improves the classical algorithm of SIFT (Fully Matching) and the modified BBF (Best-bin-first) searching algorithm (K-D Tree Searching). Our system can reduce computation by making comparisons only between the features of the same types according to features’ gradients major orientation. Hence we rebuild a database (DB) for features matching which depends entirely on orientation information. We use a lot of experiments to find out the best thresholds which suit relative methods. Our experimental results show that the proposed method can accelerate the performance of features matching without losing its accuracy. By categorizing feature points in advance, feature matching with major orientation (called MO) can effectively save 78% of the processing time. The processing time can save 88% by combining the modified BBF algorithm (where the original modified BBF can only save 46%). However, its accuracy will decrease 2% because some features are sacrificed in matching. To retain high accuracy and efficiency at once, we propose a hybrid method to find out image candidates rapidly by adopting the MO method and the modified BBF algorithm firstly, and to determine the final result by performing fully matching with image candidates. The experiment results show that this method can save 86% processing time with nearly the same accuracy.
Subjects
SIFT
K-D Tree
Major Orientation
Type
thesis
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