14.2.16.2 Support Vector Machines, SVM, Applied to Recognition

Pontil, M.[Massimiliano], Verri, A.[Alessandro],
Support Vector Machines for 3D Object Recognition,
PAMI(20), No. 6, June 1998, pp. 637-646.
IEEE DOI Link 9807
BibRef
Earlier:
Direct aspect-based 3-D object recognition,
CIAP97(II: 300-307).
WWW Version. 9709
Given a set of points a linear SVM finds the hyperplane that best divides the set (maximum distance from the plane, maximize correct classification). Support vectors are subsets of points in the classes. Apply to the same kinds of problems as appearance based matching. BibRef

Pontil, M., Rogai, S., Verri, A.,
Recognizing 3-D objects with linear support vector machines,
ECCV98(II: 469).
WWW Version. BibRef 9800

Pittore, M., Basso, C., Verri, A.,
Representing and recognizing visual dynamic events with support vector machines,
CIAP99(18-23).
IEEE DOI Link 9909
BibRef

Vishwanathan, S.V.N., Smola, A.J.[Alexander J.], Vidal, R.[René],
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes,
IJCV(73), No. 1, June 2007, pp. 95-119.
Springer DOI Link 0702
Unify all kernel learning approaches. BibRef

Song, Q.[Qing], Hu, W.J.[Wen-Jie], Xie, W.F.[Wen-Fang],
Robust support vector machine with bullet hole image classification,
SMC-C(32), No. 4, November 2002, pp. 440-448.
IEEE Top Reference. 0301
BibRef

Mantero, P., Moser, G., Serpico, S.B.,
Partially Supervised Classification of Remote Sensing Images Through SVM-Based Probability Density Estimation,
GeoRS(43), No. 3, March 2005, pp. 559-570.
IEEE Abstract. 0501
See also Conditional Copulas for Change Detection in Heterogeneous Remote Sensing Images. BibRef

Pozdnoukhov, A.[Alexei], Bengio, S.[Samy],
Invariances in kernel methods: From samples to objects,
PRL(27), No. 10, 15 July 2006, pp. 1087-1097.
WWW Version. 0606
BibRef
And:
Graph-based transformation manifolds for invariant pattern recognition with kernel methods,
ICPR06(III: 1228-1231).
IEEE DOI Link 0609
BibRef
And: ICPR06(IV: 956).
IEEE DOI Link 0609
BibRef
Earlier:
Tangent vector kernels for invariant image classification with SVMs,
ICPR04(III: 486-489).
IEEE DOI Link 0409
Kernel methods; SVM; Invariances; Tangent vectors BibRef

Mariethoz, J.[Johnny], Bengio, S.[Samy],
A kernel trick for sequences applied to text-independent speaker verification systems,
PR(40), No. 8, August 2007, pp. 2315-2324.
WWW Version. 0704
Support vector machines; Gaussian mixture models; Sequence kernel; Text-independent speaker verification BibRef

Su, L.H.[Li-Hong],
Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis,
PandRS(64), No. 4, July 2009, pp. 407-413.
Elsevier DOI Link
WWW Version. 0907
Classification; Training; Data mining; Land cover; Vegetation BibRef

Yu, Z.W.[Zhi-Wen], Wong, H.S.[Hau-San], Wen, G.H.[Gui-Hua],
A modified support vector machine and its application to image segmentation,
IVC(29), No. 1, January 2011, pp. 29-40.
Elsevier DOI Link
WWW Version. 1011
Support vector machine; Image segmentation; Classification BibRef

Li, C.H., Kuo, B.C., Lin, C.T., Huang, C.S.,
A Spatial-Contextual Support Vector Machine for Remotely Sensed Image Classification,
GeoRS(50), No. 3, March 2012, pp. 784-799.
IEEE DOI Link 1203
BibRef

Zhang, H., Shi, W., Liu, K.,
Fuzzy-Topology-Integrated Support Vector Machine for Remotely Sensed Image Classification,
GeoRS(50), No. 3, March 2012, pp. 850-862.
IEEE DOI Link 1203
BibRef

Han, R.[Ruimei], Cheng, X.Q.[Xiao-Qian], Zhang, J.[Junqi],
Study on Key Technology of HJ-1 Satellite HSI Image Processing,
ISIDF11(1-4).
IEEE DOI Link 1111
SVM classification. BibRef

Wang, X.[Xin], Luo, Y.P.[Yi-Ping], Jiang, T.[Ting], Gong, H.[Hui], Luo, S.[Sheng], Zhang, X.W.[Xiao-Wei],
A New Classification Method for LIDAR Data Based on Unbalanced Support Vector Machine,
ISIDF11(1-4).
IEEE DOI Link 1111
BibRef

Le, T.[Trung], Tran, D., Ma, W.[Wanli], Sharma, D.,
A new support vector machine method for medical image classification,
EUVIP10(165-170).
IEEE DOI Link 1110
BibRef

Lin, Y.Q.[Yuan-Qing], Lv, F.J.[Feng-Jun], Zhu, S.[Shenghuo], Yang, M.[Ming], Cour, T.[Timothee], Yu, K.[Kai], Cao, L.L.[Liang-Liang], Huang, T.[Thomas],
Large-scale image classification: Fast feature extraction and SVM training,
CVPR11(1689-1696).
IEEE DOI Link 1106
BibRef

Lei, Y.J.[Yin-Jie], Wong, W.[Wilson], Liu, W.[Wei], Bennamoun, M.[Mohammed],
An HMM-SVM-Based Automatic Image Annotation Approach,
ACCV10(IV: 115-126).
Springer DOI Link 1011
BibRef

Zaidi, N.A.[Nayyar A.], Squire, D.M.[David McG.],
Local Adaptive SVM for Object Recognition,
DICTA10(196-201).
IEEE DOI Link 1012
BibRef

Shang, C.J.[Chang-Jing], Barnes, D.[Dave],
Combining support vector machines and information gain ranking for classification of Mars McMurdo panorama images,
ICIP10(1061-1064).
IEEE DOI Link 1009
BibRef

Ramzi, P.[Pouria],
Classification of LiDAR data based on multi-class SVM,
CGC10(185).
PDF Version. 1006
BibRef

Bagarinao, E.[Epifanio], Kurita, T.[Takio], Higashikubo, M.[Masakatsu], Inayoshi, H.[Hiroaki],
Adapting SVM Image Classifiers to Changes in Imaging Conditions Using Incremental SVM: An Application to Car Detection,
ACCV09(III: 363-372).
Springer DOI Link 0909
BibRef

Gao, Y.[Yan], Choudhary, A.[Alok],
Active Learning Image Spam Hunter,
ISVC09(II: 293-302).
Springer DOI Link 0911
Gaussian and SVM approaches. Indicate only a few examples. BibRef

Wang, Y.J.[Yu-Jian], Yuan, J.Z.[Jia-Zheng], Fan, L.L.[Li-Li], Liu, Z.G.[Zhi-Guo],
Application Research of Support Vector Machine in Multi-Spectra Remote Sensing Image Classification,
CISP09(1-5).
IEEE DOI Link 0910
BibRef

Deng, Z.J.[Zi-Jian], Li, B.C.[Bi-Cheng], Zhuang, J.[Jun],
Image Object Recognition by SVMs and Evidence Theory,
CIVR05(560-567).
Springer DOI Link 0507
BibRef

Li, Y.P.[Yun-Peng], Huttenlocher, D.P.[Daniel P.],
Learning for Optical Flow Using Stochastic Optimization,
ECCV08(II: 379-391).
Springer DOI Link
PDF Version. 0810
BibRef
Earlier:
Learning for stereo vision using the structured support vector machine,
CVPR08(1-8).
IEEE DOI Link 0806
BibRef

Farrús, M.[Mireia], Ejarque, P.[Pascual], Temko, A.[Andrey], Hernando, J.[Javier],
Histogram Equalization in SVM Multimodal Person Verification,
ICB07(819-827).
Springer DOI Link 0708
BibRef

Zhang, G.X.[Ge-Xiang], Jin, W.D.[Wei-Dong], Hu, L.Z.[Lai-Zhao],
Radar emitter signal recognition based on support vector machines,
ICARCV04(II: 826-831).
IEEE DOI Link 0412
BibRef

Osuna, E., Freund, R., Girosi, F.,
Training Support Vector Machines: An Application to Face Detection,
CVPR97(130-136).
IEEE DOI Link 9704
Award, Longuet-Higgins. (Awarded 10 years later for contributions that withstood the test of time.) Similar to Poggio architecture except S.V.M. for large sets of data. Maximize margin between clusters. Similar results to Poggio except higher false positives, but faster. BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Support Vector Machines, SVM, Feature Selection .