|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
STABLE FEATURE POINTS FOR IMPROVED IMAGE RETRIEVAL AND MATCHING
Matthew Johnson and Roberto Cipolla
Local interest points and descriptors have been used very successfully to achieve accurate and efficient image retrieval and matching performance which is robust to occlusion and limited viewpoint change. Currently, these systems tend to be initialized from still images and require that a thousand or more points be stored in a retrieval data structure for each object. Many of these points are rarely if ever used, and thus unnecessarily limit the number of reference images that can be stored effectively. We propose a method for determining the stability of local interest points and their descriptors such that an efficient and effective subset of points can be stored. This technique has been shown to reduce the number of required points by an order of magnitude while improving performance, allowing for significantly smaller data structures for use in retrieval and matching.
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