TY - GEN
T1 - 3D interest point detection via discriminative learning
AU - Teran, Leizer
AU - Mordohai, Philippos
PY - 2014
Y1 - 2014
N2 - The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while designed according to human preference, can be ill-equipped for handling the variety and subjectivity in human responses. Different tasks have different requirements for interest point detection; some tasks may necessitate high precision while other tasks may require high recall. Sometimes points with high curvature may be desirable, while in other cases high curvature may be an indication of noise. Geometric methods lack the required flexibility to adapt to such changes. As a consequence, interest point detection seems to be well suited for machine learning methods that can be trained to match the criteria applied on the annotated training data. In this paper, we formulate interest point detection as a supervised binary classification problem using a random forest as our classifier. We validate the accuracy of our method and compare our results to those of five state of the art methods on a new, standard benchmark.
AB - The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while designed according to human preference, can be ill-equipped for handling the variety and subjectivity in human responses. Different tasks have different requirements for interest point detection; some tasks may necessitate high precision while other tasks may require high recall. Sometimes points with high curvature may be desirable, while in other cases high curvature may be an indication of noise. Geometric methods lack the required flexibility to adapt to such changes. As a consequence, interest point detection seems to be well suited for machine learning methods that can be trained to match the criteria applied on the annotated training data. In this paper, we formulate interest point detection as a supervised binary classification problem using a random forest as our classifier. We validate the accuracy of our method and compare our results to those of five state of the art methods on a new, standard benchmark.
KW - 3D computer vision
UR - http://www.scopus.com/inward/record.url?scp=84906519424&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906519424&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10590-1_11
DO - 10.1007/978-3-319-10590-1_11
M3 - Conference contribution
AN - SCOPUS:84906519424
SN - 9783319105895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 173
BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -