14.2.18.3 Support Vector Machines, SVM, Applied to Recognition

Chapter Contents (Back)
Support Vector Machines. SVM. 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 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., Rogai, S., Verri, A.,
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.
WWW Link. 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.
WWW Link. 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

Zhong, S.P.[Shang-Ping], Chen, D.[Daya], Xu, Q.F.[Qiao-Fen], Chen, T.S.[Tian-Shun],
Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification,
PR(46), No. 7, July 2013, pp. 2045-2054.
Elsevier DOI 1303
Gaussian kernel function; Fast kernel learning method; Two-class pattern classification; Formulated kernel target alignment criterion; Euler-Maclaurin formula; Determined global minimum point; High time efficiency 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.[Yanchun], Tan, G.[Guozhen],
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

Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., Emery, W.J.,
SVM Active Learning Approach for Image Classification Using Spatial Information,
GeoRS(52), No. 4, April 2014, pp. 2217-2233.
IEEE DOI 1403
entropy 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


Wei, Z., Hoai, M.,
Region Ranking SVM for Image Classification,
CVPR16(2987-2996)
IEEE DOI 1612
BibRef

Li, J.,
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.[Hanyun], 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.[Wanli], 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.[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 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 1011
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 .


Last update:Apr 26, 2017 at 10:20:07