Pontil, M.[Massimiliano],
Verri, A.[Alessandro],
Support Vector Machines for 3D Object Recognition,
PAMI(20), No. 6, June 1998, pp. 637-646.
IEEE DOI
9807
BibRef
Earlier:
Direct aspect-based 3-D object recognition,
CIAP97(II: 300-307).
Springer DOI
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.[Massimiliano],
Rogai, S.,
Verri, A.[Alessandro],
Recognizing 3-D objects with linear support vector machines,
ECCV98(II: 469).
Springer DOI
BibRef
9800
Pittore, M.,
Basso, C.,
Verri, A.,
Representing and recognizing visual dynamic events with support vector
machines,
CIAP99(18-23).
IEEE DOI
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
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.
Elsevier DOI
0606
BibRef
And:
Graph-based transformation manifolds for invariant pattern recognition
with kernel methods,
ICPR06(III: 1228-1231).
IEEE DOI
0609
BibRef
And:
ICPR06(IV: 956).
IEEE DOI
0609
BibRef
Earlier:
Tangent vector kernels for invariant image classification with SVMs,
ICPR04(III: 486-489).
IEEE DOI
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.
Elsevier DOI
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
0907
Classification; Training; Data mining; Land cover; Vegetation
BibRef
Karacali, B.[Bilge],
Quasi-supervised learning for biomedical data analysis,
PR(43), No. 10, October 2010, pp. 3674-3682.
Elsevier DOI
1007
Biomedical data analysis; Abnormality detection; Nearest neighbor
rule; Support vector machines; Flow cytometry; Electroencephalography
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
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
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
1203
BibRef
Nanni, L.[Loris],
Brahnam, S.[Sheryl],
Lumini, A.[Alessandra],
A simple method for improving local binary patterns by considering
non-uniform patterns,
PR(45), No. 10, October 2012, pp. 3844-3852.
Elsevier DOI
1206
Texture descriptors; Local binary patterns; Local ternary patterns;
Non-uniform patterns; Support vector machines
BibRef
Nanni, L.[Loris],
Brahnam, S.[Sheryl],
Lumini, A.[Alessandra],
Local phase quantization descriptor for improving shape
retrieval/classification,
PRL(33), No. 16, 1 December 2012, pp. 2254-2260.
Elsevier DOI
1210
Shape classification; Local phase quantization; Inner distance shape
context; Shape context; Height functions; Texture descriptors
See also Local binary patterns for a hybrid fingerprint matcher.
BibRef
Nanni, L.[Loris],
Lumini, A.[Alessandra],
Brahnam, S.[Sheryl],
Weighted Reward-Punishment Editing,
PRL(75), No. 1, 2016, pp. 48-54.
Elsevier DOI
1604
Pattern editing
BibRef
Shang, C.J.[Chang-Jing],
Shen, Q.A.[Qi-Ang],
Rough Feature Selection For Neural Network Based Image Classification,
IJIG(2), No. 4, October 2002, pp. 541-555.
0210
BibRef
Shang, C.J.[Chang-Jing],
Barnes, D.[Dave],
Fuzzy-rough feature selection aided support vector machines for Mars
image classification,
CVIU(117), No. 3, March 2013, pp. 202-213.
Elsevier DOI
1302
BibRef
Earlier:
Combining support vector machines and information gain ranking for
classification of Mars McMurdo panorama images,
ICIP10(1061-1064).
IEEE DOI
1009
Fuzzy-rough feature selection; Support vector machines; Mars terrain
images; Image classification
BibRef
Tao, D.P.[Da-Peng],
Jin, L.W.[Lian-Wen],
Liu, W.F.[Wei-Feng],
Li, X.L.[Xue-Long],
Hessian Regularized Support Vector Machines for
Mobile Image Annotation on the Cloud,
MultMed(15), No. 4, 2013, pp. 833-844.
IEEE DOI
1307
Hamming compressed sensing; mobile service;
BibRef
Sun, L.[Liang],
Ge, H.W.[Hong-Wei],
Yoshida, S.[Shinichi],
Liang, Y.C.[Yan-Chun],
Tan, G.Z.[Guo-Zhen],
Support vector description of clusters for content-based image
annotation,
PR(47), No. 3, 2014, pp. 1361-1374.
Elsevier DOI
1312
Support vector clustering
BibRef
Negri, R.G.[Rogério Galante],
Dutra, L.V.[Luciano Vieira],
Sant'Anna, S.J.S.[Sidnei Joăo Siqueira],
An innovative support vector machine based method for contextual
image classification,
PandRS(87), No. 1, 2014, pp. 241-248.
Elsevier DOI
1402
Image classification
BibRef
Irtaza, A.[Aun],
Jaffar, M.A.[M. Arfan],
Categorical image retrieval through genetically optimized support
vector machines (GOSVM) and hybrid texture features,
SIViP(9), No. 7, October 2015, pp. 1503-1519.
WWW Link.
1509
BibRef
Qi, Y.,
Zhang, G.,
Strategy of active learning support vector machine for image
retrieval,
IET-CV(10), No. 1, 2016, pp. 87-94.
DOI Link
1601
content-based retrieval
BibRef
Koda, S.,
Zeggada, A.,
Melgani, F.,
Nishii, R.,
Spatial and Structured SVM for Multilabel Image Classification,
GeoRS(56), No. 10, October 2018, pp. 5948-5960.
IEEE DOI
1810
Support vector machines, Remote sensing, Feature extraction,
Unmanned aerial vehicles, Training, Cameras, Task analysis,
unmanned aerial vehicle (UAV)
BibRef
Vapnik, V.[Vladimir],
Izmailov, R.[Rauf],
Reinforced SVM method and memorization mechanisms,
PR(119), 2021, pp. 108018.
Elsevier DOI
2106
Support vector machine, classification, learning theory,
VC dimension, kernel function, Reproducing Kernel Hilbert space
BibRef
Zhang, H.T.[Hai-Tian],
Gao, M.F.[Mao-Fang],
Ren, C.[Chao],
Feature-Ensemble-Based Crop Mapping for Multi-Temporal Sentinel-2
Data Using Oversampling Algorithms and Gray Wolf Optimizer Support
Vector Machine,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Kamkar, I.,
Gupta, S.,
Li, C.[Cheng],
Phung, D.,
Venkatesh, S.,
Stable clinical prediction using graph support vector machines,
ICPR16(3332-3337)
IEEE DOI
1705
Correlation, Indexes, Mathematical model, Optimization,
Stability criteria, Support, vector, machines
BibRef
Wei, Z.,
Hoai, M.,
Region Ranking SVM for Image Classification,
CVPR16(2987-2996)
IEEE DOI
1612
BibRef
Li, J.M.[Ji-Ming],
Active Learning for Hyperspectral Image Classification with a Stacked
Autoencoders Based Neural Network,
ICIP16(1062-1065)
IEEE DOI
1610
Hyperspectral imaging
BibRef
Sun, Z.[Zhuo],
Wang, C.[Cheng],
Li, P.[Peng],
Wang, H.Y.[Han-Yun],
Li, J.[Jonathan],
Hyperspectral Image Classification with SVM-Based Domain Adaption
Classifiers,
CVRS12(268-272).
IEEE DOI
1302
BibRef
Chernousova, E.[Elena],
Levdik, P.[Pavel],
Tatarchuk, A.[Alexander],
Mottl, V.[Vadim],
Windridge, D.[David],
Non-enumerative Cross Validation for the Determination of Structural
Parameters in Feature-Selective SVMs,
ICPR14(3654-3659)
IEEE DOI
1412
Observers
BibRef
Mu, Y.D.[Ya-Dong],
Hua, G.[Gang],
Fan, W.[Wei],
Chang, S.F.[Shih-Fu],
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual
Classification,
CVPR14(979-986)
IEEE DOI
1409
Kernel SVM; Locality sensitive hashing; random subspace
BibRef
Prakash, J.S.[J. Suriya],
Vignesh, K.A.[K. Annamalai],
Ashok, C.,
Adithyan, R.,
Multi class Support Vector Machines classifier for machine vision
application,
IMVIP12(197-199).
IEEE DOI
1302
BibRef
Wang, X.M.[Xin-Ming],
Chen, X.[Xin],
Classification of ASTER image using SVM and local spatial statistics Gi,
CVRS12(366-370).
IEEE DOI
1302
BibRef
Hu, S.W.[Shuo-Wen],
Kwon, H.S.[Hee-Sung],
Rao, R.[Raghuveer],
Robust classification using support vector machine in low-dimensional
manifold space for automatic target recognition,
AIPR11(1-4).
IEEE DOI
1204
BibRef
Han, R.M.[Rui-Mei],
Cheng, X.Q.[Xiao-Qian],
Zhang, J.Q.[Jun-Qi],
Study on Key Technology of HJ-1 Satellite HSI Image Processing,
ISIDF11(1-4).
IEEE DOI
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
1111
BibRef
Le, T.[Trung],
Tran, D.,
Ma, W.L.[Wan-Li],
Sharma, D.,
A new support vector machine method for medical image classification,
EUVIP10(165-170).
IEEE DOI
1110
BibRef
Lin, Y.Q.[Yuan-Qing],
Lv, F.J.[Feng-Jun],
Zhu, S.H.[Sheng-Huo],
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
1106
BibRef
Ramzi, P.[Pouria],
Classification of LiDAR data based on multi-class SVM,
CGC10(185).
PDF File.
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
0909
BibRef
Gao, Y.[Yan],
Choudhary, A.[Alok],
Active Learning Image Spam Hunter,
ISVC09(II: 293-302).
Springer DOI
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
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
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
PDF File.
0810
BibRef
Earlier:
Learning for stereo vision using the structured support vector machine,
CVPR08(1-8).
IEEE DOI
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
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
0412
BibRef
Osuna, E.,
Freund, R.,
Girosi, F.,
Training Support Vector Machines: An Application to Face Detection,
CVPR97(130-136).
IEEE DOI
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 .