**Phone :**

**+90 222 239 3750-3252
Fax :
+90 222 239 36 13
E-mail :
mlcvlab@ogu.edu.tr
**

SOFTWARE |

**Polyhedral Conic classifiers: ** This
is a software package for polyhedral conic classifiers. See our paper
Polyhedral Conic Classifiers for Computer Vision Applications and Open Set Recognition
by Hakan Cevikalp and Halil Saglamlar (submitted to IEEE Transactions on PAMI).
..**click here**

**Fast and Accurate Face Recognition with Image Sets: ** This
is a software package for large-scale face recognition using image sets. We use polyhedral conic classifiers for set based recognition. See our paper
Fast and Accurate Image Sets Recognition Based on Polyhedral Conic Functions
by Hakan Cevikalp and Hasan Serhan Yavuz (submitted to the Pattern Recognition).
..**click here**

**LARGE-SCALE SET-BASED FACE RECOGNITION: ** This
is a software package for large-scale face recognition using image sets. We use kernelized convex hulls to approximate image sets and and show that it is sufficient to
use only the samples that participate in shaping the image set boundaries in this setting. To find those important samples that form the image set boundaries in the feature
space, we employed the kernelized Support Vector Data Description (SVDD) method which finds a compact hypersphere that best fits the image set samples.
See our paper
Towards Large Scale Set Based Face Recognition By Using Convex Hull Models
by Hakan Cevikalp, Hasan Serhan Yavuz and Meltem Yalcin (submitted to the IEEE Transactions on Circuits and Systems for Video Technology).
..**click here**

**LARGE-SCALE IMAGE RETRIEVAL: ** This
is a software package for large-scale image retrieval. The method uses Transductive Support Vector Machines and Binary Hierachical Trees to produce compact Hash codes.
In contrast to the existing methods in the literature, the proposed method can use both the labeled and unlabaled data.
See our paper
Large-Scale Image Retrieval Using Transductive Support Vector Machines
by Hakan Cevikalp & Merve Elmas (submitted to the IEEE Transactions on Image Processing).
..**click here**

**ROBUST TRANSDUCTIVE SUPPORT VECTOR MACHINES: ** This
is a software package for Robust Transductive SVM classifier. We also provide the code for Transductive SVM classifier using Concave-Convex procedure here.
See our paper
Large-Scale Robust Transductive Support Vector Machines
by Hakan Cevikalp & Vojtech Franc (submitted to the Neurocomputing).
..**click here**

**BEST FITTING HYPERPLANES CLASSIFIERS: ** This
is a software package for best fitting hyperplanes classifiers. Here, you can find the codes for our proposed methods "1-Sided Best Fitting Hyperplane" and "2-Sided Best Fitting Hyperplane"
classifiers. We also provide the codes for other best fitting classifiers, "Generalized Eigenvalue Proximal Support Vector Machine-GEPSVM" of Mangasarian and Wild, "Twin Support Vector Machine"
of Jayadeva et al. So, this is a full package of best fitting hyperplanes classifiers.
See our paper
Best Fitting Hyperplanes for Classification
by Hakan Cevikalp (submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence).
..**click here**

**LATENT TRAINING OF CASCADE CLASSIFIERS DETECTOR: ** This
is a software package for latent training of the cascade detector of binary and one-class classifiers. By using this package, experimental results on PASCAL VOC 2007 datasets can be reproduced as described in our paper
Visual Object Detection Using Cascades of Binary and
One-Class Classifiers by Hakan Cevikalp and Bill Triggs (submitted to the IEEE Transactions on Image Processing).
..**click here**

**PEDESTRIAN DETECTOR: ** This
is a person (pedestrian) detector trained by using about 20K positive samples. It uses a cascade of classifiers that include both binary and
one-class type classifiers. We trained 4 roots detector in total. There are two roots close to being symmetric for each root detection window.
We used latent training strategy as described in our paper
Visual Object Detection Using Cascades of Binary and
One-Class Classifiers by Hakan Cevikalp and Bill Triggs (submitted to the IEEE Transactions on Image Processing).
..**click here**

**SEMI-SUPERVISED DISCRIMINATIVE
COMMON VECTOR METHOD: ** This is a distance metric learning algorithm
which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original distance metric in high-dimensional input spaces.
It uses a method based on discriminative common vector methodology using constraints instead of explicit class labels.
We also provided Matlab codes (written by us) of other tested methods such as Relevant Component Analysis (RCA), Kernel RCA,
Discriminative Component Analysis and its kernel version. Please see
the paper titled
Semi-Supervised Discriminative Cmmon Vector
Method for Computer Vision Applications
(Neurocomputing, vol. 129, pp. 289-297, 2014) by Cevikalp...**click here**

**RIGID OBJECT DETECTION USING
CASCADES OF CONVEX CLASS MODEL CLASSIFIERS: ** It includes face and people detectors
that use cascades of nearest convex model classifiers and linear SVM. In the first stage of the cascade we use a linear SVM. The rest
of the cascade includes a combination of efficient "one-class" nearest convex model classifiers including a linear hyperplane classifier,
a linear hypersphere classifier, and a nonlinear hypersphere classifier. Please see
the paper titled
Efficient Object Detection using Cascades of Nearest
Convex Model classifiers
(CVPR 2012) by Cevikalp and Triggs for more information...**click here**

**LARGE MARGIN
CLASSIFIER BASED ON HYPERDISKS: **These are
essentially binary large margin classifiers, but we use hyperdisks to
approximate classes rather than convex hulls or affine hulls. We can use kernel trick as well. For multi-class
problems one-against-rest and one-against-one procedures are used. Please see
the paper titled
Large margin classifier based on hyperdisks,
Pattern Recognition, by Cevikalp and Triggs for more information...**click here**

**SVM BASED
HIEARARCHICAL DECISION TREES: **It includes** **
two new clustering algorithms for partition of data samples for the support
vector machine based hierarchical classification. A divisive (top-down) approach
is considered in which a set of classes is automatically separated into two
smaller groups at each node of the hierarchy. Please see the paper titled
New
clustering algorithms for the support vector machine based hierarchical classification
(Pattern Recognition Letters, 2010) by Hakan Cevikalp for more
information.... **click here**

**LARGE MARGIN
CLASSIFIERS BASED ON AFFINE HULLS: **These are
essentially binary large margin classifiers, but we use affine hulls to
approximate classes rather than convex hulls. They are especially good for
high-dimensional problems and we can use kernel trick as well. For multi-class
problems one-against-rest and one-against-one procedures are used. Please see
the paper titled
Large margin classifiers based on affine hulls
(Neurocomputing, 2010) by Cevikalp et al. for more information...**click here**

**FACE
RECOGNITION BASED ON IMAGE SETS: **A novel method
for face recognition from image sets. We represent images as points in a linear
or affine feature space and characterize each image set by a convex geometric
region (the affine or convex hull) spanned by its feature points. Set
dissimilarity is measured by geometric distances between convex models. See the
paper titled
Face recognition based on image sets
(CVPR 2010) by Hakan
Cevikalp and Bill Triggs for more information... **click here **

**
DISTANCE METRIC LEARNING BY QUADRATIC PROGRAMMING BASED ON EQUIVALENCE CONSTRAINTS: ** This is a new distance metric learning algorithm
which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original distance metric in lower-dimensional input spaces.
Learning a pseudo distance metric from equivalence constraints is formulated as a quadratic optimization problem, and we
also integrate the large margin concept into the formulation. Please see
the paper titled
Distance Metric Learning by Quadratic Programming Based on Equivalence Constraints
(International Journal of Innovative Computing, Information and Control, 2012) by Cevikalp for more information...**click here**

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