We propose a novel approach to face verification based on the Error Correcting Output Coding (ECO... more We propose a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase, the client set is repeatedly divided into two ECOC specified sub-sets (super-classes) to train a set of binary classifiers. The output of the classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. As a matching score in this space, we propose the average first order Minkowski distance between the probe and gallery images. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results. q
... The original motivation for encoding multiple classifiers us-ing an error-correcting code is ... more ... The original motivation for encoding multiple classifiers us-ing an error-correcting code is based on the idea of mod-elling the prediction task as a communication problem, in which class ... In our experiments we use BCH coding method with allzero code word removed ...
We present a method for automated segmentation of the vasculature in retinal images. The method p... more We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE [1] and STARE [2] databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al. [1].
It is known that the Error Correcting Output Code (ECOC) technique can improve generalisation for... more It is known that the Error Correcting Output Code (ECOC) technique can improve generalisation for problems involving more than two classes. ECOC uses a strategy based on calculating distance to a class label in order to classify a pattern. However in some applications other kinds of information such as individual class probabilities can be useful. Least Squares(LS) is an alternative combination strategy to the standard distance based measure used in ECOC, but the effect of code specifications like the size of code or distance between labels has not been investigated in LS-ECOC framework. In this paper we consider constraints on choice of code matrix and express the relationship between final variance and local variance. Experiments on artificial and real data demonstrate that classification performance with LS can be comparable to the original distance based approach.
Two binary labelling techniques for decision-level fusion are considered for reducing correlation... more Two binary labelling techniques for decision-level fusion are considered for reducing correlation in the context of multiple classifier systems. First, we describe a method based on error correcting coding that uses binary code words to decompose a multi-class problem into a set of complementary two-class problems. We look at the conditions necessary for reduction of error and introduce a modified version that is less sensitive to code word selection. Second, we describe a partitioning method for two-class problems that transforms each training pattern into a vertex of the binary hypercube. A constructive algorithm for binary-to-binary mappings identifies a set of inconsistently classified patterns, random subsets of which are used to perturb base classifier training sets. Experimental results on artificial and real data, using a combination of simple neural network classifiers, demonstrate improvement in performance for these techniques, the first suitable for k-class problems, k>2 and the second for k=2.
Error correcting output coding (ECOC), an information theoretic concept, seems an attractive idea... more Error correcting output coding (ECOC), an information theoretic concept, seems an attractive idea for improving the performance of automatic classifiers, particularly for problems that involve large number of classes. In this paper, we look at the conditions necessary for reduction of error in this framework and introduce a new version of ECOC. To show the error reduction procedure and compare the new algorithm with traditional one, we use an artificial benchmark on which we are able to control the rate of noise to investigate the behaviour of system in different parts of input space, as well as a few real problems
AdaBoost, a recent v ersion of Boosting is known to improve the performance of decision trees in ... more AdaBoost, a recent v ersion of Boosting is known to improve the performance of decision trees in many classi cation problems, but in some cases it does not do as well as expected. There are also a few reports of its application to more complex classi ers such as neural networks. In this paper we decompose and modify this algorithm for use with RBF NNs, our methodology being based on the technique of combining multiple classi ers.
We propose a novel approach to face identification and verification based on the Error Correcting... more We propose a novel approach to face identification and verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (super-classes) to train a set of binary classifiers. The output of the classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. As a matching score in this space we propose the average first order Minkowski distance between the probe and gallery images. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results.
We propose a novel approach to face verification based on the Error Correcting Output Coding (ECO... more We propose a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase, the client set is repeatedly divided into two ECOC specified sub-sets (super-classes) to train a set of binary classifiers. The output of the classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. As a matching score in this space, we propose the average first order Minkowski distance between the probe and gallery images. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results. q
... The original motivation for encoding multiple classifiers us-ing an error-correcting code is ... more ... The original motivation for encoding multiple classifiers us-ing an error-correcting code is based on the idea of mod-elling the prediction task as a communication problem, in which class ... In our experiments we use BCH coding method with allzero code word removed ...
We present a method for automated segmentation of the vasculature in retinal images. The method p... more We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE [1] and STARE [2] databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al. [1].
It is known that the Error Correcting Output Code (ECOC) technique can improve generalisation for... more It is known that the Error Correcting Output Code (ECOC) technique can improve generalisation for problems involving more than two classes. ECOC uses a strategy based on calculating distance to a class label in order to classify a pattern. However in some applications other kinds of information such as individual class probabilities can be useful. Least Squares(LS) is an alternative combination strategy to the standard distance based measure used in ECOC, but the effect of code specifications like the size of code or distance between labels has not been investigated in LS-ECOC framework. In this paper we consider constraints on choice of code matrix and express the relationship between final variance and local variance. Experiments on artificial and real data demonstrate that classification performance with LS can be comparable to the original distance based approach.
Two binary labelling techniques for decision-level fusion are considered for reducing correlation... more Two binary labelling techniques for decision-level fusion are considered for reducing correlation in the context of multiple classifier systems. First, we describe a method based on error correcting coding that uses binary code words to decompose a multi-class problem into a set of complementary two-class problems. We look at the conditions necessary for reduction of error and introduce a modified version that is less sensitive to code word selection. Second, we describe a partitioning method for two-class problems that transforms each training pattern into a vertex of the binary hypercube. A constructive algorithm for binary-to-binary mappings identifies a set of inconsistently classified patterns, random subsets of which are used to perturb base classifier training sets. Experimental results on artificial and real data, using a combination of simple neural network classifiers, demonstrate improvement in performance for these techniques, the first suitable for k-class problems, k>2 and the second for k=2.
Error correcting output coding (ECOC), an information theoretic concept, seems an attractive idea... more Error correcting output coding (ECOC), an information theoretic concept, seems an attractive idea for improving the performance of automatic classifiers, particularly for problems that involve large number of classes. In this paper, we look at the conditions necessary for reduction of error in this framework and introduce a new version of ECOC. To show the error reduction procedure and compare the new algorithm with traditional one, we use an artificial benchmark on which we are able to control the rate of noise to investigate the behaviour of system in different parts of input space, as well as a few real problems
AdaBoost, a recent v ersion of Boosting is known to improve the performance of decision trees in ... more AdaBoost, a recent v ersion of Boosting is known to improve the performance of decision trees in many classi cation problems, but in some cases it does not do as well as expected. There are also a few reports of its application to more complex classi ers such as neural networks. In this paper we decompose and modify this algorithm for use with RBF NNs, our methodology being based on the technique of combining multiple classi ers.
We propose a novel approach to face identification and verification based on the Error Correcting... more We propose a novel approach to face identification and verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (super-classes) to train a set of binary classifiers. The output of the classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. As a matching score in this space we propose the average first order Minkowski distance between the probe and gallery images. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results.
Uploads
Papers by reza ghaderi