TY - GEN
T1 - Auto-context modeling using multiple Kernel learning
AU - Song, Huan
AU - Thiagarajan, Jayaraman J.
AU - Ramamurthy, Karthikeyan Natesan
AU - Spanias, Andreas
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - In complex visual recognition systems, feature fusion has become crucial to discriminate between a large number of classes. In particular, fusing high-level context information with image appearance models can be effective in object/scene recognition. To this end, we develop an auto-context modeling approach under the RKHS (Reproducing Kernel Hilbert Space) setting, wherein a series of supervised learners are used to approximate the context model. By posing the problem of fusing the context and appearance models using multiple kernel learning, we develop a computationally tractable solution to this challenging problem. Furthermore, we propose to use the marginal probabilities from a kernel SVM classifier to construct the auto-context kernel. In addition to providing better regularization to the learning problem, our approach leads to improved recognition performance in comparison to using only the image features.
AB - In complex visual recognition systems, feature fusion has become crucial to discriminate between a large number of classes. In particular, fusing high-level context information with image appearance models can be effective in object/scene recognition. To this end, we develop an auto-context modeling approach under the RKHS (Reproducing Kernel Hilbert Space) setting, wherein a series of supervised learners are used to approximate the context model. By posing the problem of fusing the context and appearance models using multiple kernel learning, we develop a computationally tractable solution to this challenging problem. Furthermore, we propose to use the marginal probabilities from a kernel SVM classifier to construct the auto-context kernel. In addition to providing better regularization to the learning problem, our approach leads to improved recognition performance in comparison to using only the image features.
KW - Feature fusion
KW - Image classification
KW - Marginalized kernel
KW - Multiple kernel learning
UR - http://www.scopus.com/inward/record.url?scp=85006722228&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006722228&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7532682
DO - 10.1109/ICIP.2016.7532682
M3 - Conference contribution
AN - SCOPUS:85006722228
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1868
EP - 1872
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -