TY - GEN
T1 - Bayesian structural content abstraction for image authentication using Markov Pixon model
AU - Feng, Wei
AU - Liu, Zhi-Qiang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - We present a hierarchical representation of image structure and use it for image content authentication. Firstly, we model the image with the Markov pixon random field. Within the Bayesian framework, the optimal label map and regional pixon map can be obtained, based on which we define a non-directed graph, or namely Bayesian Structural Content Abstraction (BaSCA). This representation captures the spatial topology information of homogeneous regions as well as their finest scale and interactions. Then, an efficient optimization scheme has been proposed to iteratively minimize the learning error to all content-identical image samples generated by an acceptable operation set defined by the user. In addition, we use the regional pixon map to remove spurious vertices and thus to establish a BaSCA hierarchy naturally. The BaSCA itself and its features can act as the signature of the protected image. Our experimental results show that the proposed approach has much less false positive and comparable false negative probability compared with the existing methods. © 2005 IEEE.
AB - We present a hierarchical representation of image structure and use it for image content authentication. Firstly, we model the image with the Markov pixon random field. Within the Bayesian framework, the optimal label map and regional pixon map can be obtained, based on which we define a non-directed graph, or namely Bayesian Structural Content Abstraction (BaSCA). This representation captures the spatial topology information of homogeneous regions as well as their finest scale and interactions. Then, an efficient optimization scheme has been proposed to iteratively minimize the learning error to all content-identical image samples generated by an acceptable operation set defined by the user. In addition, we use the regional pixon map to remove spurious vertices and thus to establish a BaSCA hierarchy naturally. The BaSCA itself and its features can act as the signature of the protected image. Our experimental results show that the proposed approach has much less false positive and comparable false negative probability compared with the existing methods. © 2005 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=28444493337&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-28444493337&origin=recordpage
U2 - 10.1109/ICMLC.2005.1527878
DO - 10.1109/ICMLC.2005.1527878
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780390911
SN - 078039092
SN - 9780780390928
VL - 9
T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
SP - 5290
EP - 5296
BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
PB - IEEE Computer Society
T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005
Y2 - 18 August 2005 through 21 August 2005
ER -