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Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose...
We present a regularization technique based on the minimum description length (MDL) principle for the linear manifold clustering. We suggest an inexact minimum description length method based on describing the data structure as linear manifold clusters. We examine the behavior of the proposed method and compare it performance against simulated clustering results of various dimensionality and structure...
Computing a background model from a given sequence of video frames is a prerequisite for many computer vision applications. Recently, this problem has been posed as learning a low-dimensional subspace from high dimensional data. Many contemporary subspace segmentation methods have been proposed to overcome the limitations of the methods developed for simple background scenes. Unfortunately, because...
Very few research works have addressed the problem of directly manipulating raw HDR vectors for general HDR image processing. In this paper a framework is proposed towards this aim and is based on a new representation of HDR images in the form of an ordering of vectors and an index image. This enables to formulate vector-preserving image processing methods dedicated to HDR images. The ordering relies...
In multiview image stitching, the colors of images in a scene might vary when images are taken under different illumination or camera settings. A common way to produce a seamless stitched image is to transform the colors of a target image to match that of a source image. In this paper we present a color transfer method based on two premises: first, pixels in the generated image should have similar...
Considering the graph of a feature function as an embedded surface in three dimensions is a standard device in computer vision. When multiple feature functions (eg. multiple images) are available, the natural extension of the above concept is to a higher-dimensional embedded surface. This has received surprisingly little attention. In this paper, we advocate for this view by showing the utility of...
We present theoretical results showing that deep neural networks require fewer parameters than a shallow network to achieve similar accuracy results on a simple classification problem where the decision boundary is a circle in two dimensions. In particular, shallow networks require O(1/√ϵ) parameters compared to O(log2[1/ϵ]) for a deep network to achieve an error rate of ϵ.
Graph-based semi-supervised learning has recently come into focus for to its two defining phases: graph construction, which converts the data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. And the label inference is based on the smoothness assumption of semi-supervised learning. In this study, we propose an enhanced label inference...
Pattern recognition tasks such as the data classification and clustering usually can be represented by the perspective of multiple views or feature spaces. Obviously, the accuracy of the classification and clustering should be greatly improved if we carefully consider the discriminabilities from multiple views and explore the complementary information among them. However, multiple features also bring...
A comprehensive framework for detection and characterization of partial intrinsic symmetry over 3D shapes is proposed. To identify prominent symmetric regions which overlap in space and vary in form, the proposed framework is decoupled into a Correspondence Space Voting (CSV) procedure followed by a Transformation Space Mapping (TSM) procedure. In the CSV procedure, significant symmetries are first...
Spectral clustering is able to extract clusters with various characteristics without a parametric model, however it is infeasible for large datasets due to its high computational cost and memory requirement. Approximate spectral clustering (ASC) addresses this challenge by a representative-based partitioning approach which first finds a set of data representatives either by sampling or quantization,...
We propose a novel geometric framework for analyzing spontaneous facial expressions, with the specific goal of comparing, matching, and averaging the shapes of landmarks trajectories. Here we represent facial expressions by the motion of the landmarks across the time. The trajectories are represented by curves. We use elastic shape analysis of these curves to develop a Riemannian framework for analyzing...
Kernel principal component analysis (kPCA) learns nonlinear modes of variation in the data by nonlinearly mapping the data to kernel feature space and performing (linear) PCA in the associated reproducing kernel Hilbert space (RKHS). However, several widely-used Mercer kernels map data to a Hilbert sphere in RKHS. For such directional data in RKHS, linear analyses can be unnatural or suboptimal. Hence,...
Subspace clustering refers to the task of clustering a collection of points drawn from a high-dimensional space into a union of multiple subspaces that best fits them. State-of-the-art approaches have been proposed for tackling this clustering problem by using the low-rank or sparse optimization techniques. However, most of the traditional subspace clustering methods are developed for single-view...
Recently, sparse representation (SR) over a redundant dictionary has become a popular way of representing the data. It has been verified as an efficient and useful tool to promote the discrimination between signals. This work develops a joint learning approach to find the low dimensional discriminative features for high dimensional data. To avoid the high computational cost of direct sparse coding...
We present a new method for analyzing data manifolds based on Weyls tube theorem. The coefficients of the tube polynomial for a manifold provide geometric information such as the volume of the manifold or its Euler characteristic, thus providing bounds on the geometric nature of the manifold. We present an algorithm estimating the coefficients of the tube polynomial for a given manifold and demonstrate...
Although the design of low-level local spatiotemporal features has recently led to significant improvement of performance in many action recognition applications, much less attention has been given to the equally important problem how to organize such low-level features extracted from the videos into a higher-level representation suitable to represent and discriminate between many different action...
In this paper, we introduce a nonlinear dimensionality reduction (NLDR) technique that can construct a low-dimensional embedding efficiently and accurately with low embedding distortions. The key idea is to divide NLDR into nonlinearity reduction and linear dimensionality reduction, which simplifies the overall NLDR process. Nonlinearity reduction is based on the elastic shell model that measures...
This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi-supervised learning of a graph describing a manifold of anatomical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually...
Dimension reduction is one of the most important issues in machine learning and computational intelligence. Typical data sets are point clouds in a high dimensional space with a hidden structure to be found in low dimensional submanifolds. Finding this intrinsic manifold structure is very important in the understanding of the data and for reducing computational complexity. In this paper, we propose...
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