TY - JOUR
T1 - Feature extraction and evaluation using edge histogram descriptor in MPEG-7
AU - Won, Ghee Sun
PY - 2004
Y1 - 2004
N2 - According to the definition of the edge histogram descriptor (EHD) in MPEG-7, one can easily generate an extra histogram bin from the 5-bin local edge histogram of each 4 x 4 sub-image. This extra histogram bin defines the ratio of the non-edge area (i.e., monotonous region) in the sub-image. Forming a feature vector with 6 edge/non-edge types, we can generate 33 different feature vectors (or 33 x 6 = 198 feature elements) including 16 vectors from 4x4 sub-images, 1 vector from a global histogram, 13 vectors from semi-global histograms, 1 vector from entropy, and 2 vectors from centers of gravity. A statistical hypothesis testing is employed to see which feature vectors/elements are most informative to differentiate different image classes. Experimental results show that non-edge and entropy features are the most informative features among all 33/198 feature vectors/elements.
AB - According to the definition of the edge histogram descriptor (EHD) in MPEG-7, one can easily generate an extra histogram bin from the 5-bin local edge histogram of each 4 x 4 sub-image. This extra histogram bin defines the ratio of the non-edge area (i.e., monotonous region) in the sub-image. Forming a feature vector with 6 edge/non-edge types, we can generate 33 different feature vectors (or 33 x 6 = 198 feature elements) including 16 vectors from 4x4 sub-images, 1 vector from a global histogram, 13 vectors from semi-global histograms, 1 vector from entropy, and 2 vectors from centers of gravity. A statistical hypothesis testing is employed to see which feature vectors/elements are most informative to differentiate different image classes. Experimental results show that non-edge and entropy features are the most informative features among all 33/198 feature vectors/elements.
UR - http://www.scopus.com/inward/record.url?scp=35048899709&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30543-9_73
DO - 10.1007/978-3-540-30543-9_73
M3 - Article
AN - SCOPUS:35048899709
SN - 0302-9743
VL - 3333
SP - 583
EP - 590
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -