485 research outputs found
Angular feature extraction and ensemble classification method for 2D, 2.5D and 3D face recognition.
It has been recognised that, within the context of face recognition, angular separation between centred feature vectors is a useful measure of dissimilarity. In this thesis we explore this observation in more detail and compare and contrast angular separation with the Euclidean, Manhattan and Mahalonobis distance metrics. This is applied to 2D, 2.5D and 3D face images and the investigation is done in conjunction with various feature extraction techniques such as local binary patterns (LBP) and linear discriminant analysis (LDA). We also employ error-correcting output code (ECOC) ensembles of support vector machines (SVMs) to project feature vectors non-linearly into a new and more discriminative feature space. It is shown that, for both face verification and face recognition tasks, angular separation is a more discerning dissimilarity measure than the others. It is also shown that the effect of applying the feature extraction algorithms described above is to considerably sharpen and enhance the ability of all metrics, but in particular angular separation, to distinguish inter-personal from extra-personal face image differences. A novel technique, known as angularisation, is introduced by which a data set that is well separated in the angular sense can be mapped into a new feature space in which other metrics are equally discriminative. This operation can be performed separately or it can be incorporated into an SVM kernel. The benefit of angularisation is that it allows strong classification methods to take advantage of angular separation without explicitly incorporating it into their construction. It is shown that the accuracy of ECOC ensembles can be improved in this way. A further aspect of the research is to compare the effectiveness of the ECOC approach to constructing ensembles of SVM base classifiers with that of binary hierarchical classifiers (BHC). Experiments are performed which lead to the conclusion that, for face recognition problems, ECOC yields greater classification accuracy than the BHC method. This is attributed primarily to the fact that the size of the training set decreases along a path from the root node to a leaf node of the BHC tree and this leads to great difficulties in constructing accurate base classifiers at the lower nodes
Convex Optimization for Binary Classifier Aggregation in Multiclass Problems
Multiclass problems are often decomposed into multiple binary problems that
are solved by individual binary classifiers whose results are integrated into a
final answer. Various methods, including all-pairs (APs), one-versus-all (OVA),
and error correcting output code (ECOC), have been studied, to decompose
multiclass problems into binary problems. However, little study has been made
to optimally aggregate binary problems to determine a final answer to the
multiclass problem. In this paper we present a convex optimization method for
an optimal aggregation of binary classifiers to estimate class membership
probabilities in multiclass problems. We model the class membership probability
as a softmax function which takes a conic combination of discrepancies induced
by individual binary classifiers, as an input. With this model, we formulate
the regularized maximum likelihood estimation as a convex optimization problem,
which is solved by the primal-dual interior point method. Connections of our
method to large margin classifiers are presented, showing that the large margin
formulation can be considered as a limiting case of our convex formulation.
Numerical experiments on synthetic and real-world data sets demonstrate that
our method outperforms existing aggregation methods as well as direct methods,
in terms of the classification accuracy and the quality of class membership
probability estimates.Comment: Appeared in Proceedings of the 2014 SIAM International Conference on
Data Mining (SDM 2014
Deep Convolutional Neural Network Ensembles using ECOC
Deep neural networks have enhanced the performance of decision making systems
in many applications including image understanding, and further gains can be
achieved by constructing ensembles. However, designing an ensemble of deep
networks is often not very beneficial since the time needed to train the
networks is very high or the performance gain obtained is not very significant.
In this paper, we analyse error correcting output coding (ECOC) framework to be
used as an ensemble technique for deep networks and propose different design
strategies to address the accuracy-complexity trade-off. We carry out an
extensive comparative study between the introduced ECOC designs and the
state-of-the-art ensemble techniques such as ensemble averaging and gradient
boosting decision trees. Furthermore, we propose a combinatory technique which
is shown to achieve the highest classification performance amongst all.Comment: 13 pages double column IEEE transactions styl
Deep Convolutional Neural Network Ensembles Using ECOC
Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this paper, we analyse an error correcting output coding (ECOC) framework for constructing ensembles of deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a fusion technique, that is shown to achieve the highest classification performance
Deep N-ary Error Correcting Output Codes
Ensemble learning consistently improves the performance of multi-class
classification through aggregating a series of base classifiers. To this end,
data-independent ensemble methods like Error Correcting Output Codes (ECOC)
attract increasing attention due to its easiness of implementation and
parallelization. Specifically, traditional ECOCs and its general extension
N-ary ECOC decompose the original multi-class classification problem into a
series of independent simpler classification subproblems. Unfortunately,
integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as
deep N-ary ECOC, is not straightforward and yet fully exploited in the
literature, due to the high expense of training base learners. To facilitate
the training of N-ary ECOC with deep learning base learners, we further propose
three different variants of parameter sharing architectures for deep N-ary
ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct
experiments by varying the backbone with different deep neural network
architectures for both image and text classification tasks. Furthermore,
extensive ablation studies on deep N-ary ECOC show its superior performance
over other deep data-independent ensemble methods.Comment: EAI MOBIMEDIA 202
Face Verification Competition on the XM2VTS Database
In the year 2000 a competition was organised to collect face verification results on an identical, publicly available data set using a standard evaluation protocol. The database used was the Xm2vts database along with the Lausanne protocol [14]. Four different institutions submitted results on the database which were subsequently published in [13]. Three years later, a second contest using the same dataset and protocol was organised as part of AVBPA 2003. This time round seven seperate institutions submitted results to the competition. This paper presents the results of the competition and shows that verification results on this protocol have increased in performance by a factor of 3
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