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Fast Image Classification with Reduced Multiclass Support Vector Machines [chapter]

Marco Melis, Luca Piras, Battista Biggio, Giorgio Giacinto, Giorgio Fumera, Fabio Roli
2015 Lecture Notes in Computer Science  
Support Vector Machines (SVMs) have been successfully exploited to tackle this problem, using one-vs-one or one-vs-all learning schemes to enable multiclass classification, and kernels designed for image  ...  Image classification is intrinsically a multiclass, nonlinear classification task.  ...  This work has been partly supported by the project "Advanced and secure sharing of multimedia data over social networks in the future Internet" (CUP F71J11000690002) funded by Regione Autonoma della Sardegna  ... 
doi:10.1007/978-3-319-23234-8_8 fatcat:bhtiomjhpjhb5faow3egjrarym

Progressive refinement for support vector machines

Kiri L. Wagstaff, Michael Kocurek, Dominic Mazzoni, Benyang Tang
2009 Data mining and knowledge discovery  
Support vector machines (SVMs) have good accuracy and generalization properties, but they tend to be slow to classify new examples.  ...  We construct two SVMs: a "full" SVM that is optimized for high accuracy, and an approximation SVM (via reduced-set or subset methods) that provides extremely fast, but less accurate, classifications.  ...  We gratefully acknowledge the support of a grant from the NASA Advanced Information Systems Technology program and grant #IIS-0705681 from the National Science Foundation.  ... 
doi:10.1007/s10618-009-0149-y fatcat:3zk36wecmvgphhtg2ucmbrqday

Face recognition with positive and negative samples using support vector machine

A.D. Chitra, P. Ponmuthuramalingam
2016 ACCENTS Transactions on Image Processing and Computer Vision  
In this research work, support vector machine (SVM) algorithm detects face from the input image with less amount of false detection rate.  ...  1 Support vector machine SVM is treated as a supervised machine learning algorithm (shown in Figure 1 ).  ... 
doi:10.19101/tipcv.2016.23003 fatcat:qi7ecz2cfbesborjc6awv7adda

Multiclass reduced-set support vector machines

Benyang Tang, Dominic Mazzoni
2006 Proceedings of the 23rd international conference on Machine learning - ICML '06  
There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy.  ...  This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be "shared" between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors.  ...  Fast queryoptimized kernel machine classification via incremental approximate nearest support vectors. Proc. of the 20th International Conference on Machine Learning. Washington, DC.  ... 
doi:10.1145/1143844.1143960 dblp:conf/icml/TangM06 fatcat:5suxorskcjdcvfzk2eywzjbeye

A fast two-stage classification method of support vector machines

Jin Chen, Cheng Wang, Runsheng Wang
2008 2008 International Conference on Information and Automation  
In this paper, we present a fast two-stage method of support vector machines, which includes a feature reduction algorithm and a fast multiclass method.  ...  Moreover, a simple method is proposed to reduce the processing time of multiclass problems, where one binary SVM with the fewest support vectors (SVs) will be selected iteratively to exclude the less similar  ...  In order to further reduce the computation complexity, a simple method called fast OAO (FOAO) is proposed to combine C-1 binary SVMs with the fewest support vectors.  ... 
doi:10.1109/icinfa.2008.4608121 fatcat:bb4via74efc3djwwgxji7kgmuq

Support Vector Machine Applications in Terahertz Pulsed Signals Feature Sets

Xiaoxia Yin, Brian W.-H. Ng, Bernd M. Fischer, Bradley Ferguson, Derek Abbott
2007 IEEE Sensors Journal  
Support vector machine (SVM) learning algorithms are sufficiently powerful to detect patterns hidden inside noisy biomedical measurements.  ...  This paper introduces a frequency orientation component method to extract T-ray feature sets for the application of two-and multiclass classification using SVMs.  ...  The small number of support vectors greatly reduces the computational burden of the classification task.  ... 
doi:10.1109/jsen.2007.908243 fatcat:37gynr22vvcqli54jzgae24z4e

Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine

Melvin Daniel, Jangkung Raharjo, Koredianto Usman
2020 Engineering Journal  
And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space.  ...  This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM).  ...  Support Vector Machine Classification SVM as a classification method becomes very popular lately. It is used not only for classification but also as a regression technique.  ... 
doi:10.4186/ej.2020.24.5.135 fatcat:6g5dljhvybhhzozhwdrnq23zdu

3D Object Recognition using Multiclass Support Vector Machine-K-Nearest Neighbor Supported by Local and Global Feature

J.
2012 Journal of Computer Science  
The multi-classs SVM-KNN classifier is applied to the feature vector to recognize the object. The proposed method uses the COIL-100 and CALTECH image databases for its experimentation.  ...  The results of the proposed method are better when comparing with other methods like KNN, SVM and BPN.  ...  Singh et al. (2010) uses the support vector machine with the local features for classifying the leaf images.  ... 
doi:10.3844/jcssp.2012.1380.1388 fatcat:hneczsrl6ffp5jcjsw5gq7zp5i

Static Hand Gesture Recognition using an Android Device

Tejashri J.Joshi, Shiva Kumar, N. Z. Tarapore, Vivek Mohile
2015 International Journal of Computer Applications  
The recognition approach used in this paper is based on Support Vector Machine (SVM). Proposed Hand Gesture System is location and orientation invariant.  ...  The need to enhance communication between humans and computers has been instrumental in determining new communication models, and accordingly new ways of interacting with machines.  ...  Like PCA Support Vector Machine also needed setup procedure. These features are used to train multiclass Support Vector Machine to properly classify the input image.  ... 
doi:10.5120/21356-4348 fatcat:j2utnggxavcqvgkghwq2apg2yi

Adaptive binary tree for fast SVM multiclass classification

Jin Chen, Cheng Wang, Runsheng Wang
2009 Neurocomputing  
This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM).  ...  binary SVMs with the fewest average number of support vectors (SVs).  ...  Acknowledgments This work was supported in part by the China Scholarship Council (CSC) and in part by the Innovation Foundation for the excellent Ph.D. students at National University of Defense Technology  ... 
doi:10.1016/j.neucom.2009.03.013 fatcat:6zigpun7wrfwvdzt4tnt2lh7jy

Automatic Genre Categorization of Emails into predefined categories using machine learning

Vinod Kumar Bhalla, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
In today's dynamic world, there is a need for fast, efficient, and reliable means of communication. To meet these requirements email system was developed and it got popular with the invention of WWW.  ...  It has been observed in the proposed research that the classification of emails greatly improves efficiency and saves time and effort to manage them.  ...  Fig5 Support vectors New data is feed to use the previously learned features to decide the category of the data. Support vector machine (SVM) can also be used to train the multiclass classifier.  ... 
doi:10.17762/turcomat.v12i2.2302 fatcat:h4mbsqkeknce3a7d2w3s3j4xqi

A Hybrid Boosted-SVM Classifier for Recognizing Parts of 3D Objects

Omar Herouane, Lahcen Moumoun, Taoufiq Gadi, Mohamed Chahhou
2018 International Journal of Intelligent Engineering and Systems  
The training of the classification model is based on a significant and robust feature vector representing parts of the 3D object, the Shape Spectrum Descriptor (SSD) is used for this purpose.  ...  Support vector machines algorithm 2.1 Support vector machines Support Vector Machines are powerful methodologies for solving linear or nonlinear classification or regression problems [23] .  ...  In the next section, we introduce the Support Vector Machine algorithm and the strategy used for the multiclass-SVM.  ... 
doi:10.22266/ijies2018.0430.12 fatcat:yffnyuv6qfafla3skiqerqtory

Improving SVM classification accuracy using a hierarchical approach for hyperspectral images

Begm Demir, Sarp Ertrk
2009 2009 16th IEEE International Conference on Image Processing (ICIP)  
Because classification with all support vectors is only utilized at the lowest resolution and classification of higher resolutions requires a subset of the support vectors, this approach reduces the overall  ...  Initially, conventional SVM classification is carried out in the highest hierarchical level (lowest resolution) using all support vectors and a one-toone multiclass classification strategy, so that all  ...  Kernel based hyperspectral image classification algorithms such as support vector machines (SVMs) [1] [2] [3] and relevance vector machines (RVMs) [4] have been shown to provide higher classification  ... 
doi:10.1109/icip.2009.5414491 dblp:conf/icip/DemirE09 fatcat:s6utakfxx5d2zf3zktnfqewkma

Numeric Digit Classification Using HOG Feature Space and Multiclass Support Vector Machine Classifier

Kiran Banjare, Prof. Sampada Massey
2016 International Journal Of Scientific Research And Education  
For the efficient classification of the HOG features of numeric digits, a linear multiclass Support Vector Machine (SVM) classifier has been proposed, because it has better responses for nonlinear classification  ...  This paper proposed an efficient image appearance feature based approach which process the acquired digit image using Histogram of Oriented Gradients (HOG).  ...  Fig. 5 : 5 HOG features for some handwritten numbers 2.5 Support Vector Machine (SVM) In machine learning, support vector machines (SVM) are supervised learning models with associated learning algorithms  ... 
doi:10.18535/ijsre/v4i05.08 fatcat:p2u5xzuduzfh3pccmskpc37dy4

Steganalysis using logistic regression

Ivans Lubenko, Andrew D. Ker, Nasir D. Memon, Jana Dittmann, Adnan M. Alattar, Edward J. Delp III
2011 Media Watermarking, Security, and Forensics III  
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis.  ...  LR offers more information than traditional SVM methods -it estimates class probabilities as well as providing a simple classification -and can be adapted more easily and efficiently for multiclass problems  ...  There is an additional advantage, which is fast multiclass classification.  ... 
doi:10.1117/12.872245 dblp:conf/mediaforensics/LubenkoK11 fatcat:t6bt6ycwyrdbdenfzmdswt2lfu
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