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IEEE Transactions on Pattern Analysis and Machine Intelligence

Yong-Jin Liu, Minjing Yu, Bing-Jun Li, Ying He
Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions with high intensity or colour variation and large superpixels in content-sparse regions. Rather than using the conventional SLIC method that clusters pixels in , we map the input image to a 2-dimensional manifold , whose area elements are a good measure of the content density in ...
March 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Bumsub Ham, Minsu Cho, Jean Ponce
Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input image, restoring noisy or altered image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input images, and that it is not robust to outliers...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D Lee
We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Yung-Kyun Noh, Jihun Hamm, Frank Chongwoo Park, Byoung-Tak Zhang, Daniel D Lee
Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Alykhan Tejani, Rigas Kouskouridas, Andreas Doumanoglou, Danhang Tang, Tae-Kyun Kim
In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the art template matching feature into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. We train with positive samples only and we treat class distributions at the leaf nodes as latent variables. During testing we infer by iteratively updating these distributions, providing accurate estimation of background clutter and foreground occlusions and, thus, better detection rate...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Bing Su, Xiaoqing Ding, Hao Wang, Ying Wu
Since the observables at particular time instants in a temporal sequence exhibit dependencies, they are not independent samples. Thus, it is not plausible to apply i.i.d. assumption-based dimensionality reduction methods to sequence data. This paper presents a novel supervised dimensionality reduction approach for sequence data, called Linear Sequence Discriminant Analysis (LSDA). It learns a linear discriminative projection of the feature vectors in sequences to a lower-dimensional subspace by maximizing the separability of the sequence classes such that the entire sequences are holistically discriminated...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Na Qi, Yunhui Shi, Xiaoyan Sun, Jingdong Wang, Baocai Yin, Junbin Gao
Traditional synthesis/analysis sparse representation models signals in a one dimensional (1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D modeling cannot sufficiently handle MD signals of high dimensionality in limited computational resources and memory usage, as breaking the data structure and inherently ignores the diversity of MD signals (tensors). We utilize the multilinearity of tensors to establish the redundant basis of the space of multi linear maps with the sparsity constraint, and further propose MD synthesis/analysis sparse models to effectively and efficiently represent MD signals in their original form...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Julian Straub, Oren Freifeld, Guy Rosman, John J Leonard, John W Fisher
Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches utilize these regularities via the restrictive, and rather local, Manhattan World (MW) assumption which posits that every plane is perpendicular to one of the axes of a single coordinate system. The aforementioned regularities are especially evident in the surface normal distribution of a scene where they manifest as orthogonally-coupled clusters...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Wenguan Wang, Jianbing Shen, Ruigang Yang, Fatih Porikli
Video saliency, aiming for estimation of a single dominant object in a sequence, offers strong object-level cues for unsupervised video object segmentation. In this paper, we present a geodesic distance based technique that provides reliable and temporally consistent saliency measurement of superpixels as a prior for pixel-wise labeling. Using undirected intra-frame and inter-frame graphs constructed from spatiotemporal edges or appearance and motion, and a skeleton abstraction step to further enhance saliency estimates, our method formulates the pixel-wise segmentation task as an energy minimization problem on a function that consists of unary terms of global foreground and background models, dynamic location models, and pairwise terms of label smoothness potentials...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Martti Kesaniemi, Kai Virtanen
This paper presents two new computationally efficient direct methods for fitting n-dimensional ellipsoids to noisy data. They conduct the fitting by minimizing the algebraic distance in subject to suitable quadratic constraints. The hyperellipsoid-specific (HES) method is an elaboration of existing ellipse and 3D ellipsoid-specific fitting methods. It is shown that HES is ellipsoid-specific in n-dimensional space. A limitation of HES is that it may provide biased fitting results with data originating from an ellipsoid with a large ratio between the longest and shortest main axis...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Feng Lu, Xiaowu Chen, Imari Sato, Yoichi Sato
We propose uncalibrated photometric stereo methods that address the problem due to unknown isotropic reflectance. At the core of our methods is the notion of "constrained half-vector symmetry" for general isotropic BRDFs. We show that such symmetry can be observed in various real-world materials, and it leads to new techniques for shape and light source estimation. Based on the 1D and 2D representations of the symmetry, we propose two methods for surface normal estimation; one focuses on accurate elevation angle recovery for surface normals when the light sources only cover the visible hemisphere, and the other for comprehensive surface normal optimization in the case that the light sources are also non-uniformly distributed...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Mehrtash Harandi, Mathieu Salzmann, Richard Hartley
Representing images and videos with Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, has been shown to yield high discriminative power in many visual recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices -especially of high-dimensional ones- comes at a high cost that limits the applicability of existing techniques. In this paper, we introduce algorithms able to handle high-dimensional SPD matrices by constructing a lower-dimensional SPD manifold...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Behrooz Nasihatkon, Fredrik Kahl
We study the relation between the correlation-based target functions of low-resolution and high-resolution intensity-based registration for the class of rigid transformations. Our results show that low-resolution target values can tightly bound the high-resolution target function in natural images. This can help with analyzing and better understanding the process of multiresolution image registration. It also gives a guideline for designing multiresolution algorithms in which the search space in higher resolution registration is restricted given the fitness values for lower resolution image pairs...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Suren Vagharshakyan, Robert Bregovic, Atanas Gotchev
In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras. Our approach utilizes sparse representation of epipolar-plane images (EPI) in shearlet transform domain. The shearlet transform has been specifically modified to handle the straight lines characteristic for EPI. The devised iterative regularization algorithm based on adaptive thresholding provides high-quality reconstruction results for relatively big disparities between neighboring views...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Wen-Yan Lin, Fan Wang, Ming-Ming Cheng, Sai-Kit Yeung, Philip H S Torr, Minh N Do, Jiangbo Lu
A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Lacey Best-Rowden, Anil K Jain
The two underlying premises of automatic face recognition are uniqueness and permanence. This paper investigates the permanence property by addressing the following: Does face recognition ability of state-of-the-art systems degrade with elapsed time between enrolled and query face images? If so, what is the rate of decline w.r.t. the elapsed time? While previous studies have reported degradations in accuracy, no formal statistical analysis of large-scale longitudinal data has been conducted. We conduct such an analysis on two mugshot databases, which are the largest facial aging databases studied to date in terms of number of subjects, images per subject, and elapsed times...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, Lei Zhang
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Zhouchen Lin, Chen Xu, Hongbin Zha
-norm based low rank matrix factorization in the presence of missing data and outliers remains a hot topic in computer vision. Due to non-convexity and non-smoothness, all the existing methods either lack scalability or robustness, or have no theoretical guarantee on convergence. In this paper, we apply the Majorization Minimization technique to solve this problem. At each iteration, we upper bound the original function with a strongly convex surrogate. By minimizing the surrogate and updating the iterates accordingly, the objective function has sufficient decrease, which is stronger than just being non-increasing that other methods could offer...
January 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Bing Li, Chunfeng Yuan, Weihua Xiong, Weiming Hu, Houwen Peng, Xinmiao Ding, Steve Maybank
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL...
December 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang
Given a single outdoor image, we propose a collaborative learning approach using novel weather features to label the image as either sunny or cloudy. Though limited, this two-class classification problem is by no means trivial given the great variety of outdoor images captured by different cameras where the images may have been edited after capture. Our overall weather feature combines the data-driven convolutional neural network (CNN) feature and well-chosen weather-specific features. They work collaboratively within a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification...
December 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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