TY - GEN
T1 - Structured ordinal features for appearance-based object representation
AU - Liao, Shengcai
AU - Lei, Zhen
AU - Li, Stan Z.
AU - Yuan, Xiaotong
AU - He, Ran
PY - 2007
Y1 - 2007
N2 - In this paper, we propose a novel appearance-based representation, called Structured Ordinal Feature (SOF). SOF is a binary string encoded by combining eight ordinal blocks in a circle symmetrically. SOF is invariant to linear transformations on images and is flexible enough to represent different local structures of different complexity. We further extend SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position. In this way, MSOF encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation. We also present an efficient algorithm for computing MSOF using integral images. Based on MSOF, statistical analysis and learning are performed to select most effective features and construct classifiers. The proposed method is evaluated with face recognition experiments, in which we achieve a high rank-1 recognition rate of 98.24% on FERET database.
AB - In this paper, we propose a novel appearance-based representation, called Structured Ordinal Feature (SOF). SOF is a binary string encoded by combining eight ordinal blocks in a circle symmetrically. SOF is invariant to linear transformations on images and is flexible enough to represent different local structures of different complexity. We further extend SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position. In this way, MSOF encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation. We also present an efficient algorithm for computing MSOF using integral images. Based on MSOF, statistical analysis and learning are performed to select most effective features and construct classifiers. The proposed method is evaluated with face recognition experiments, in which we achieve a high rank-1 recognition rate of 98.24% on FERET database.
UR - http://www.scopus.com/inward/record.url?scp=38149090987&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149090987&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-75690-3_14
DO - 10.1007/978-3-540-75690-3_14
M3 - Conference contribution
AN - SCOPUS:38149090987
SN - 9783540756897
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 183
EP - 192
BT - Analysis and Modeling of Faces and Gestures - Third International Workshop, AMFG 2007, Proceedings
PB - Springer Verlag
T2 - 3rd International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2007
Y2 - 20 October 2007 through 20 October 2007
ER -