Cascaded classification with optimal candidate selection for effective place recognition

A two-stage cascaded classification approach with an optimal candidate selection scheme is proposed to recognize places using images taken by camera phones. An optimal acceptance threshold is chosen to maximize the probability of accepting more positives and rejecting more negatives at the first stage so that an optimal number of candidates are selected. The first classifier is trained using simple color and texture features. The second classifier is trained by scale invariant feature transform (SIFT). For a query image, a number of matching candidates are selected using k nearest neighbor at the first stage and passed on to the second stage for a refining classification to select the best matching result. The searching range is narrowed down dynamically at the second stage depending on the output of the first stage. Experimental results show that this method is promising by improving the recognition accuracy and reducing the computation time.