Two-stage Classification System Combining Model-basedApproach and Support Vector Machines

The motivation of this work is based on two key observations. First, the classification algorithms can be separated into two main categories : discriminative and model-based approaches. Second, two types of patterns can generate problems : ambiguous patterns and outliers. While, the first approach tries to minimize the first type of error, but cannot deal effectively with outliers, the model-based approaches make the outlier detection possible, but are not sufficiently discriminant. Thus, we propose to combine these two different approaches in a two-stage classification system embedded in a probabilistic framework. In the first stage we pre-estimate the posterior probabilities with a model-based approach and we re-estimate only the highest probabilities with appropriate Support Vector Machine (SVM) in the second stage. Another advantage of this combination is to reduce the principal burden of SVM : the processing time necessary to make a decision. Finally, the first experiments on the benchmark database MNIST have shown that our dynamic classification process allows to maintain the accuracy of SVMs, while decreasing complexity by a factor 8.7 and making the outlier rejection available.Il est possible de distinguer deux types de données pouvant causer des problèmes à un classifieur : les données ambiguës et les données aberrantes. À ces deux types d’erreurs peuvent être associés deux types de rejet : le rejet d’ambiguïté et le rejet d’ignorance. Or, si les approches de classification agissant par séparation sont mieux adaptées au premier type de rejet, elles s’avèrent peu efficaces pour traiter les données aberrantes. Par contre, les approches qui agissent par modélisation sont par nature mieux adaptées à ce second type de rejet, mais ne s’avèrent que peu discriminantes. Ainsi, nous proposons de combiner les deux types d’approche au sein d’un système de classification à deux niveaux de décision. Au premier niveau, une approche par modélisation sera utilisée pour rejeter les données aberrantes et pré-estimer les probabilités a posteriori. En cas de conflit entre plusieurs classes, des machines à vecteurs de support (SVM) appropriées seront utilisées pour ré-estimer plus précisément les probabilités des classes en conflit et permettre de rejeter efficacement les données ambiguës. En outre, cette combinaison présente l’avantage de réduire la complexité de calcul associée à la prise de décision des SVM. Ainsi, les résultats obtenus sur un problème classique de reconnaissance d’images de chiffres manuscrits isolés ont montré qu’il est possible de maintenir les performances associées aux SVM, tout en réduisant la complexité d’un facteur 8.7 et en permettant de filtrer efficacement les données aberrantes

FOCUSR: Feature oriented correspondence using spectral regularization -A method for precise surface matching

Existing methods for surface matching are limited by the trade-off between precision and computational efficiency. Here we present an improved algorithm for dense vertex-to-vertex correspondence that uses direct matching of features defined on a surface and improves it by using spectral correspondence as a regularization. This algorithm has the speed of both feature matching and spectral matching while exhibiting greatly improved precision (distance errors of 1.4%). The method, FOCUSR, incorporates implicitly such additional features to calculate the correspondence and relies on the smoothness of the lowest-frequency harmonics of a graph Laplacian to spatially regularize the features. In its simplest form, FOCUSR is an improved spectral correspondence method that nonrigidly deforms spectral embeddings. We provide here a full realization of spectral correspondence where virtually any feature can be used as additional information using weights on graph edges, but also on graph nodes and as extra embedded coordinates. As an example, the full power of FOCUSR is demonstrated in a real case scenario with the challenging task of brain surface matching across several individuals. Our results show that combining features and regularizing them in a spectral embedding greatly improves the matching precision (to a sub-millimeter level) while performing at much greater speed than existing methods

Historical documents dating using multispectral imaging and ordinal classification

The estimation of the age of undated old manuscripts is one of the most challenging and controversial tasks in the field of historical document analysis. Several dating methods have been proposed, but most of them either use destructive techniques or rely on the textual information of documents. In this work, we rather focus our attention on the discoloration and the changes in the optical proprieties of their writing materials, which are a natural phenomenon that occurs as they age. Thus, we present a new content independent and non-destructive approach based on multispectral imaging combined with a ranking classification technique, to track the spectral responses of iron-gall ink at different wavelengths over time. We evaluated the proposed approach on multispectral images of real handwritten letters dating from the 17th to the 20th century. Experimental results demonstrate the effectiveness of multispectral imaging for document images dating.The work for this paper was supported by the Natural Sciences and Engineering Research Council of Canada NSERC Discovery 05230-2019, and the National Priorities Research Program (NPRP) , grant N.NPRP 7-442-1-082 from QNRF, the Qatar National Research Fund (a member of Qatar Foundation).Scopu

Fast Brain Matching with Spectral Correspondence

Abstract. Brain matching is an important problem in neuroimaging studies. Current surface-based methods for cortex matching and atlasing, although quite accurate, can require long computation times. Here we propose an approach based on spectral correspondence, where spectra of graphs derived from the surface model meshes are matched. Cerebral cortex matching problems can thus benefit from the tremendous speed advantage of spectral methods, which are able to calculate a cortex matching in seconds rather than hours. Moreover, spectral methods are extended in order to use additional information that can improve matching. Additional information, such as sulcal depth, surface curvature, and cortical thickness can be represented in a flexible way into graph node weights (rather than only into graph edge weights) and as extra embedded coordinates. In control experiments, cortex matching becomes almost perfect when using additional information. With real data from 12 subjects, the results of 288 correspondence maps are 88 % equivalent to (and strongly correlated with) the correspondences computed with FreeSurfer, a leading computational tool used for cerebral cortex matching. Our fast and flexible spectral correspondence method could open new possibilities for brain studies that involve different types of information and that were previously limited by the computational burden.

FOCUSR: Feature Oriented Correspondence Using Spectral Regularization--A Method for Precise Surface Matching

Existing methods for surface matching are limited by the trade-off between precision and computational efficiency. Here we present an improved algorithm for dense vertex-to-vertex correspondence that uses direct matching of features defined on a surface and improves it by using spectral correspondence as a regularization. This algorithm has the speed of both feature matching and spectral matching while exhibiting greatly improved precision (distance errors of 1.4%). The method, FOCUSR, incorporates implicitly such additional features to calculate the correspondence and relies on the smoothness of the lowest-frequency harmonics of a graph Laplacian to spatially regularize the features. In its simplest form, FOCUSR is an improved spectral correspondence method that nonrigidly deforms spectral embeddings. We provide here a full realization of spectral correspondence where virtually any feature can be used as additional information using weights on graph edges, but also on graph nodes and as extra embedded coordinates. As an example, the full power of FOCUSR is demonstrated in a real case scenario with the challenging task of brain surface matching across several individuals. Our results show that combining features and regularizing them in a spectral embedding greatly improves the matching precision (to a sub-millimeter level) while performing at much greater speed than existing methods