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In this work, we are focusing on facial image clustering techniques applied on stereoscopic videos. We introduce a novel spectral clustering algorithm which combines two well-known algorithms: normalized cuts and spectral clustering. Furthermore, we introduce two approach for evaluating the similarities between facial images, one based on Mutual Information and other based on Local Binary Patterns,...
In this work we propose the use of local binary patterns in combination with double spectral analysis for facial image clustering applied to 3D (stereoscopic) videos. Double spectral clustering involves the fusion of two well known algorithms: Normalized cuts and spectral clustering in order to improve the clustering performance. The use of local binary patterns upon selected fiducial points on the...
In this work we proposed a new variant of spectral clustering using double spectral analysis which proved to be able to achieve better clustering results. The present work focuses on the special case of 3D videos and the implication of their use. Various improvements are introduced including the use of stereo over mono videos, the use of double spectral clustering over spectral clustering and the...
In this paper a novel variant of the Normalized Nut (N-Cut) clustering algorithm that incorporates imposed constraints is implemented and evaluated on facial image clustering for 3D video analysis. The clustering problem is seen as a graph cut problem through a similarity matrix representing the relation among the vertices, i.e. facial images in this work. Mutual Information is used as similarity...
In this paper a new variation of Support Vector Machines (SVM) is introduced. The proposed method is called Subclass Support Vector Machine (SSVM) and makes use of principles from Discriminant Analysis field using subclasses. The major difference over SVM is that it takes into account the existence of subclasses in the classes and tries to minimize the distribution of the samples within each subclass...
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