An analysis of SIFT object recognition with an emphasis on landmark detection

In this thesis, I explore the realm of feature-based object. recognition applied to landmark detection. I have built a system using SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data set. In addition, I have, performed a relatively extensive empirical comparison between Locality-Sensitive Hashing and Best-Bin First, two approximate nearest neighbor searches, finding that Locality-Sensitive Hashing in general performs the best.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Ross, Benjamin Charles

Advisor dc:contributor.advisor

• Trevor J. Darrell.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/33341

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/33341