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We present a novel local shape descriptor by means of General Adaptive Neighborhoods (GANs) based on the properties of the heat diffusion process on a Riemannian manifold. The GAN is a spatial region, surrounding the feature point and fitting its local shape structure, which is isometric. Our signature, called the Heat Propagation Contours (HPCs), is obtained by analysing the well-known heat kernel...
Representing images and videos by covariance descriptors and leveraging the inherent manifold structure of Symmetric Positive Definite (SPD) matrices leads to enhanced performances in various visual recognition tasks. However, when covariance descriptors are used to represent image sets, the result is often rank-deficient. Thus, most existing approaches adhere to blind perturbation with predefined...
Object recognition and pose estimation are two fundamental problems in the field of computer vision. Recognizing objects and their poses/viewpoints are critical components of ample vision and robotic systems. Multiple viewpoints of an object lie on an intrinsic low-dimensional manifold in the input space (i.e. descriptor space). Different objects captured from the same set of viewpoints have manifolds...
Understanding the role of genetics in diseases is a challenging process that has multiple applications within functional genomics and precision medicine. In this paper, we present a general clustering method to identify disease genes under a multi-view setting. First, by incorporating the graph Laplacian of spectral clustering (SC) into the discriminative K-means, we obtain a single-view subspace...
Sparse-representation is well-known for its promising performance in face recognition task. Recently, researchers have focused on optimizing the dictionary by learning the discriminative sparse model. On the other hand, symmetric positive definite (SPD) matrix descriptor has spurred great interest among computer vision community due to its inherent merits that enables features fusion. However SPD...
Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we introduce Geodesic Convolutional Neural Networks (GCNN), a generalization of the convolutional neural networks (CNN) paradigm to non-Euclidean manifolds. Our construction is based on a local geodesic system of polar...
Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method...
Regression based on hyperspectral remote sensing data contains two-fold complications, i.e., lack of labeled data and difficulty in collecting quantitative ground-truth. In this paper, we propose semi-supervised subspace learning methods for regression based on a generalized eigenvalue problem. The methods exploit abundant unlabeled data for low-dimensional subspace learning. Quantitative target values...
In image segmentation, the shape knowledge of the object may be used to guide the segmentation process. From a training set of representative shapes, a statistical model can be constructed and used to constrain the segmentation results. The shape space is usually constructed with tools such such as principal component analysis (PCA). However the main assumption of PCA that shapes lie a linear space...
Laplacian support vector machine could utilize the unlabeled samples for semi-supervised learning by applying the manifold regularization term. But the data adjacent graph in the manifold regularization term couldn't take advantage of the label information and the empirical setting of heat kernel parameter would also degrade the learning performance. Inspired by human behavioral learning theory, a...
The proximal classifier with consistency (PCC) isan improvement of generalized eigenvalue proximal support vector machine (GEPSVM), ensuring consistency ignored inGEPSVM. However, similar to many other machine learning methods, PCC uses only the global information and the eigenvalue problem need to be solved, which can not classify small sample size (SSS) problem effectively. By exploiting both global...
In many Data Analysis tasks, one deals with data that are presented in high-dimensional spaces. In practice original high-dimensional data are transformed into lower-dimensional representations (features) preserving certain subject-driven data properties such as distances or geodesic distances, angles, etc. Preserving as much as possible available information contained in the original high-dimensional...
Facial pose grouping plays an important role in the video face recognition. In this paper, we present an unsupervised facial pose grouping approach via Garbor subspace affinity and self-tuning spectral clustering. First, we utilize the local normalization method to reduce the impact of uneven illuminations, and then extract the discriminative appearance features via Gabor wavelet representation. Next,...
In this paper we propose a kernelized version of the Flexible Manifold Embedding (FME) framework. This latter has been recently proposed as a semi-supervised graph-based label propagation method that optimally estimates the labels of data and finds at the same time a linear regression function that can easily predict labels of unseen data points. The contribution of our proposed Kernel Flexible Manifold...
Lipreading techniques have shown bright prospects for speech recognition under noisy environments and for hearing-impaired listeners. In this paper, we discuss a feature extraction method based on the homeomorphic manifold analysis for lipreading. Given a set of image sequences, we think there is an underlying low dimensional unified manifold embedded in the visual space, and each image sequence can...
Classical unmixing algorithms focus primarily on scenarios with a single mixture. These techniques are easily extensible in the case of images with multiple discrete mixtures (i.e. no shared endmembers). Unmixing in scenarios with multiple mixtures with shared or common endmembers is significantly harder. Manifold clustering and embedding seem tailor-made for such a scenario, but generally these algorithms...
A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since...
In this paper, we present a head pose estimation method for unconstrained images using feature-based manifold embedding. The main challenge of manifold embedding methods is to learn a similarity kernel that is reflective of variations only due to head pose and ignore other sources of variation. To address this challenge, we have used the feature correspondences of identity-invariant Geometric Blur...
The Internet produces massive financial unstructured textual information every day. How to utilize these unstructured data effectively is a challenging topic. In the background of A share T+0 and stock option promoting in the China security market, we present a model to recognize the risk and investment opportunity according to the massive online financial textual information. Since the key word vector...
This paper investigated approaches using Isomap to extract nonlinear intrinsic features for K-Nearest Neighbor (KNN) to classify Hyperspectral data, and proposed a new classification method, Isomap-based kernel-KNN (IKNN), based on the kernel function of Isomap and kernelized-KNN classifier. This method takes advantage of the global manifold learning ability of Isomap algorithm directly, without explicitly...
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