The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We propose a fast and efficient two-stage hypothesis filtering technique that can improve performance of clustering based robust multi-model fitting algorithms. Sampling based hypothesis generation is nondeterministic and permits little control over generating poor model hypotheses, often leading to a significant proportion of bad hypotheses. Our novel filtering approach leverages the asymmetry in...
A discriminative dictionary learning algorithm is proposed to find sparse signal representations using relative attributes as the available semantic information. In contrast, existing (discriminative) dictionary learning (DDL) approaches mostly utilize binary label information to enhance the discriminative property of the signal reconstruction residual, the sparse coding vectors or both. Compared...
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
Although non-local image denoising has attracted much research effort due to its superior performance, little attention has focused on its color extension. Most existing non-local color image denoising methods process the color channels of an input image separately. However, in order to improve the performance of color image denoising, all color channels should be processed jointly for fully utilizing...
A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve image segmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whose nodes are superpixels divided with various parameters. Also, we set an edge weight to be proportional to the similarity of color and depth features between two adjacent nodes. Then,...
Zero-shot learning (ZSL) aims to classify the objects without any training samples. Direct Attribute Prediction (DAP) gives a solution with attribute space but it makes the assumption of attribute independence. To relax this assumption and consider the relation among attributes, Joint Attribute Chain Prediction (JACP) algorithm is proposed in this paper. It estimates the joint probability of attribute...
This paper presents a novel approach to detecting crowd groups and learning semantic regions with a Gestalt laws-based similarity. Different from the existing approaches based on optical flows or complete trajectories, our model adopts tracklets as the original input, because they carry more detailed information. Though those tracklets do not appear in the same duration, they are more robust to noise...
Large-scale 3D point clouds have been actively used in many applications with the advent of capturing devices. In this paper, we propose a novel saliency detection algorithm for large-scale colored 3D point clouds which capture real-world scenes. We first voxelize an input point cloud, and then partition voxels into a supervoxel which corresponds to a clusters at the lowest level. We construct the...
Video co-segmentation typically refers to the task to jointly segment common objects existing in a given group of videos. In practice, high-dimensional data such as videos are often conceptually thought of being drawn from a union of subspaces corresponding to multiple categories. Therefore, segmenting data into respective subspaces, known as subspace clustering, has widespread applications in computer...
This paper describes a novel scheme to reduce the quantization noise of compressed videos and improve the overall coding performances. The proposed scheme first consists in clustering noisy patches of the compressed sequence. Then, at the encoder side, linear mappings are learned for each cluster between the noisy patches and the corresponding source patches. The linear mappings are then transmitted...
Motion information is a key factor for action recognition and has been eagerly pursued for decades. How to effectively learn motion features in Convolutional Networks (ConvNets) remains an open issue. Prevalent ConvNets often take several full frames of video as input at a time, which can be a heavy burden for network training. In this paper, we introduce a novel framework called Tube ConvNets, by...
Many applications benefit from sampling algorithms where a small number of well chosen samples are used to generalize different properties of a large dataset. In this paper, we use diverse sampling for streaming video summarization. Several emerging applications support streaming video, but existing summarization algorithms need access to the entire video which requires a lot of memory and computational...
Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition and total variation minimization. The proposed method is designed based on the assumption that the background part...
Adaptive sparse representation has been heavily exploited in signal processing and computer vision. Recently, sparsifying transform learning received interest for its cheap computation and optimal updates in the alternating algorithms. In this work, we develop a methodology for learning a Flipping and Rotation Invariant Sparsifying Transform, dubbed FRIST, to better represent natural images that contain...
We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters...
This paper introduces a generalization of the Fisher vectors to the Riemannian manifold. The proposed descriptors, called Riemannian Fisher vectors, are defined first, based on the mixture model of Riemannian Gaussian distributions. Next, their expressions are derived and they are applied in the context of texture image classification. The results are compared to those given by the recently proposed...
This work proposes a trajectory clustering-based approach for segmenting flow patterns in high density crowd videos. The goal is to produce a pixel-wise segmentation of a video sequence (static camera), where each segment corresponds to a different motion pattern. Unlike previous studies that use only motion vectors, we extract full trajectories so as to capture the complete temporal evolution of...
Vojta-therapy is a useful technique for the treatment of physical and mental impairments in humans, and is very effective for children of less than 6 months. During the therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes the previously blocked connections between the spinal cord and brain...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.