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This paper proposes the Anchored Kernel Metric Learning (AKML) method, which learns metrics by sparsely combining locally discriminative rank-1 basis matrices in the anchored kernel induced space. First, we apply k-means clustering to generate anchored samples, thus forming an anchored sample matrix. Based on the assumption that the basis matrices can be formulated as linear combinations of anchored...
We present a biologically motivated manifold learning framework for image set classification inspired by Independent Component Analysis for Grassmann manifolds. A Grassmann manifold is a collection of linear subspaces, such that each subspace is mapped on a single point on the manifold. We propose constructing Grassmann subspaces using Independent Component Analysis for robustness and improved class...
Existing maximum-margin support vector machines (SVMs) generate a hyperplane which produces the clearest separation between positive and negative feature vectors. These SVMs are effective when datasets are large. However, when few training samples are available, the hyperplane is easily influenced by outliers that are geometrically located in the opposite class. We propose a modified SVM which weights...
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression...
This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching...
Person reidentification is a problem of recognizing a person across non-overlapping camera views. Pose variations, illumination conditions, low resolution images, and occlusion are the main challenges encountered in reidentification. Due to the uncontrolled environment in which the videos are captured, people could appear in different poses and due to which the appearance of a person could vary significantly...
Matching specific persons across scenes, known as person re-identification, is an important yet unsolved computer vision problem. Feature representation and metric learning are two fundamental factors in person re-identification. However, current person re-identification methods, which use single handcrafted feature with corresponding metric, could be not powerful enough when facing illumination,...
Associating groups of people across non-overlapping camera views is an important but unsolved problem. Compared with the similar person re-identification task, group re-identification introduces some new challenges, such as significant deformation in uncontrolled directions, great intra-group occlusions and so on. In this paper, we propose a novel patch matching based framework for group re-identification...
In this paper, we present a novel perceptually-based optimization for the improvement of stereoscopic video coding efficiency. The main idea of this proposed scheme is to adaptively adjust the quantization parameter by taking into account the Human Visual System perceptual characteristics. For this, a saliency map is generated from both views and then segmented into salient and non-salient regions...
This paper proposes a novel model for contrast enhancement of RGB images. The average local contrast measure is increased within a variational framework which preserves the hue of the original image by coupling the channels. The user is enabled to intuitively control the level of the contrast as well as the scale of the enhanced details. Moreover, our model avoids large modifications of the original...
The research of printed source identification is generally processed by scanned images which are limited by the scanner resolution. The accuracy of source identification is also bound by this limitation. In this study, microscopic images are used for printed source identification based on its high magnification capability for detailed texture and structure information. To explore the relationship...
Stochastic models of images are very useful for applications such as segmentation, deblurring, and reconstruction. Sometimes it is important to be able to simulate, or draw samples from, a stochastic image model. For example, simulation can be used as an optimization tool for segmenting, deblurring, or reconstructing an image. Also, simulation of images that characterize a system can be helpful in...
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...
This paper presents a method for dataset manipulation based on Mixed Integer Linear Programming (MILP). The proposed optimization can narrow down a dataset to a particular size, while enforcing specific distributions across different dimensions. It essentially leverages the redundancies of an initial dataset in order to generate more compact versions of it, with a specific target distribution across...
In this paper, we propose to learn object representations with inference from temporal correlation in videos to achieve effective visual tracking. Unlike traditional methods which perform feature learning either at image level or based on intuitive temporal constraint, we employ the recurrent network with Long Short Term Memory (LSTM) units to directly learn temporally correlated representations of...
Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function. In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer. In the absence of large datasets with ground...
We propose a novel method for the demosaicing of event-based images that offers substantial performance improvement of far-distance gesture recognition based on deep Convolutional Neural Network. Unlike the conventional demosaicing technique using the spatial color interpolation of Bayer patterns, our new approach utilizes spatiotemporal correlation between pixel arrays, whereby timestamps of high-resolution...
We consider the fully automated behavior understanding through visual cues in industrial environments. In contrast to most existing work, which relies on domain knowledge to construct complex handcrafted features from inputs, we exploit a Convolutional Neural Network (CNN), which is a type of deep model and can act directly on the raw inputs, to automate the process of feature construction. Although...
The main purpose of transfer learning is to resolve the problem of different data distribution, generally, when the training samples of source domain are different from the training samples of the target domain. Prediction of salient areas in natural video suffers from the lack of large video benchmarks with human gaze fixations. Different databases only provide dozens up to one or two hundred of...
Conventional Image Retargeting methods aim to preserve the salient regions in an image using As Similar as Possible (ASAP) energy formulation or As Rigid as Possible (ARAP) energy formulation. ASAP energy formulation preserves the shape of the salient object while the scale of salient object can get distorted in the retargeted image. On the contrary, ARAP energy formulation preserves the scale of...
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