TY - JOUR
T1 - Statistical properties of bit-plane probability model and its application in supervised texture classification
AU - Choy, S. K.
AU - Tong, C. S.
N1 - Funding Information:
Manuscript received September 11, 2007; revised March 28, 2008. First published June 13, 2008; last published July 11, 2008 (projected). This work was supported in part by HKBU’s Centre for Mathematical Imaging and Vision, in part by a HKBU Faculty Research Grant, and in part by a CERG Grant (201806). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Srdjan Stankovic.
PY - 2008/8
Y1 - 2008/8
N2 - The modeling of wavelet subband histograms via the product Bernoulli distributions (PBD) has received a lot of interest and the PBD model has been applied successfully in texture image retrieval. In order to fully understand the usefulness and effectiveness of the PBD model and its associated signature, namely, the bit-plane probability (BP) signature on image processing applications, we discuss and investigate some of their statistical properties. These properties would help to clarify the sufficiency of the BP signature to characterize wavelet subbands, which, in turn, justifies its use in real time applications. We apply the BP signature on supervised texture classification problem and experimental results suggest that the weighted rm L1-norm (rather than the standard L1-norm) should be used for the BP signature. Comparative classification experiments show that our method outperforms the current state-of-the-art Generalized Gaussian Density approaches.
AB - The modeling of wavelet subband histograms via the product Bernoulli distributions (PBD) has received a lot of interest and the PBD model has been applied successfully in texture image retrieval. In order to fully understand the usefulness and effectiveness of the PBD model and its associated signature, namely, the bit-plane probability (BP) signature on image processing applications, we discuss and investigate some of their statistical properties. These properties would help to clarify the sufficiency of the BP signature to characterize wavelet subbands, which, in turn, justifies its use in real time applications. We apply the BP signature on supervised texture classification problem and experimental results suggest that the weighted rm L1-norm (rather than the standard L1-norm) should be used for the BP signature. Comparative classification experiments show that our method outperforms the current state-of-the-art Generalized Gaussian Density approaches.
KW - Bit-plane probabilities (BPs)
KW - Generalized Gaussian Density (GGD)
KW - Sufficient statistics
KW - Texture classification
UR - http://www.scopus.com/inward/record.url?scp=48349118657&partnerID=8YFLogxK
U2 - 10.1109/TIP.2008.925370
DO - 10.1109/TIP.2008.925370
M3 - Journal article
C2 - 18632348
AN - SCOPUS:48349118657
SN - 1057-7149
VL - 17
SP - 1399
EP - 1405
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 8
ER -