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IEEE Transactions on Pattern Analysis and Machine Intelligence

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https://www.readbyqxmd.com/read/28333620/binary-online-learned-descriptors
#1
Vassileios Balntas, Lilian Tang, Krystian Mikolajczyk
We propose a novel approach to generate a binary descriptor optimized for each image patch independently. The approach is inspired by the linear discriminant embedding that simultaneously increases inter and decreases intra class distances. A set of discriminative and uncorrelated binary tests is established from all possible tests in an offline training process. The patch adapted descriptors are then efficiently built online from a subset of features which lead to lower intra-class distances and thus, to a more robust descriptor...
March 20, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28320652/spectral-learning-for-supervised-topic-models
#2
Yong Ren, Yining Wang, Jun Zhu
Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA)...
March 15, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28320651/collaborative-active-visual-recognition-from-crowds-a-distributed-ensemble-approach
#3
Gang Hua, Chengjiang Long, Ming Yang, Yan Gao
Active learning is an effective way of engaging users to interactively train models for visual recognition more efficiently. The vast majority of previous works focused on active learning with a single human oracle. The problem of active learning with multiple oracles in a collaborative setting has not been well explored. We present a collaborative computational model for active learning with multiple human oracles, the input from whom may be subject to different levels of noise. It leads to not only an ensemble kernel machine that is robust to label noise, but also a principled label quality measure to online detect irresponsible labelers...
March 15, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28333621/reconstructing-evolving-tree-structures-in-time-lapse-sequences-by-enforcing-time-consistency
#4
Przemyslaw Glowacki, Miguel Amavel Pinheiro, Agata Mosinska, Engin Turetken, Daniel Lebrecht, Anthony Holtmaat, Raphael Sznitman, Jan Kybic, Pascal Fua
We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously process all images in a sequence, as opposed to reconstructing structures of interest in each image independently. We formulate the problem as a Quadratic Mixed Integer Program and demonstrate the additional robustness that comes from using all available visual clues at once, instead of working frame by frame...
March 10, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28320650/svbrdf-invariant-shape-and-reflectance-estimation-from-a-light-field-camera
#5
Ting-Chun Wang, Manmohan Chandraker, A Alyosha Efros, Ravi Ramamoorthi
Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals or plastics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Our key theoretical insight is a novel analysis of diffuse plus single-lobe SVBRDFs under a light-field setup...
March 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28287961/expression-invariant-age-estimation-using-structured-learning
#6
Zhongyu Lou, Fares Alnajar, Jose Alvarez, Ninghang Hu, Theo Gevers
In this paper, we investigate and exploit the influence of facial expressions on automatic age estimation. Different from existing approaches, our method jointly learns the age and expression by introducing a new graphical model with a latent layer between the age/expression labels and the features. This layer aims to learn the relationship between the age and expression and captures the face changes which induce the aging and expression appearance, and thus obtaining expression-invariant age estimation. Conducted on three age-expression datasets (FACES [8], Lifespan [20] and NEMO [7]), our experiments illustrate the improvement in performance when the age is jointly learnt with expression in comparison to expression-independent age estimation...
March 8, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28287960/clustering-millions-of-faces-by-identity
#7
Charles Otto, Dayong Wang, Anil Jain
Given a large collection of unlabeled face images, we address the problem of clustering faces into an unknown number of identities. This problem is of interest in social media, law enforcement, and other applications, where the number of faces can be of the order of hundreds of million, while the number of identities (clusters) can range from a few thousand to millions. To address the challenges of run-time complexity and cluster quality, we present an approximate Rank-Order clustering algorithm that performs better than popular clustering algorithms (k-Means and Spectral)...
March 7, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28287959/watch-n-patch-unsupervised-learning-of-actions-and-relations
#8
Chenxia Wu, Jiemi Zhang, Ozan Sener, Bart Selman, Silvio Savarese, Ashutosh Saxena
There is a large variation in the activities that humans perform in their everyday lives. We consider modeling these composite human activities which comprises multiple basic level actions in a completely unsupervised setting. Our model learns high-level co-occurrence and temporal relations between the actions. We consider the video as a sequence of short-term action clips, which contains human-words and object-words. An activity is about a set of action-topics and object-topics indicating which actions are present and which objects are interacting with...
March 7, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28287958/multi-task-learning-with-low-rank-attribute-embedding-for-multi-camera-person-re-identification
#9
Chi Su, Fan Yang, Shiliang Zhang, Qi Tian, Larry S Davis, Wen Gao
We propose Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) to address the problem of person re-identification on multi-cameras. Re-identifications on different cameras are considered as related tasks, which allows the shared information among different tasks to be explored to improve the re-identification accuracy. The MTL-LORAE framework integrates low-level features with mid-level attributes as the descriptions for persons. To improve the accuracy of such description, we introduce the low-rank attribute embedding, which maps original binary attributes into a continuous space utilizing the correlative relationship between each pair of attributes...
March 7, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28287956/fast-supervised-discrete-hashing
#10
Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan
Learning-based hashing algorithms are "hot topics" because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called "fast supervised discrete hashing" (FSDH) based on "supervised discrete hashing" (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm...
March 7, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28287957/algebraic-clustering-of-affine-subspaces
#11
Manolis Tsakiris, Rene Vidal
Subspace clustering is an important problem in machine learning with many applications in computer vision and pattern recognition. Prior work has studied this problem using algebraic, iterative, statistical, low-rank and sparse representation techniques. While these methods have been applied to both linear and affine subspaces, theoretical results have only been established in the case of linear subspaces. For example, algebraic subspace clustering (ASC) is guaranteed to provide the correct clustering when the data points are in general position and the union of subspaces is transversal...
March 6, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28278459/fast-randomized-singular-value-thresholding-for-low-rank-optimization
#12
Tae-Hyun Oh, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon
Rank minimization can be converted into tractable surrogate problems, such as Nuclear Norm Minimization (NNM) and Weighted NNM (WNNM). The problems related to NNM, or WNNM, can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT), or Weighted SVT, but they suffer from high computational cost of Singular Value Decomposition (SVD) at each iteration. We propose a fast and accurate approximation method for SVT, that we call fast randomized SVT (FRSVT), with which we avoid direct computation of SVD...
March 3, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28278458/deep-unfolding-for-topic-models
#13
Jen-Tzung Chien, Chao-Hsi Lee
Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively...
March 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28278457/collaborative-index-embedding-for-image-retrieval
#14
Wengang Zhou, Houqiang Li, Jian Sun, Qi Tian
In content-based image retrieval, SIFT feature and the feature from deep convolutional neural network (CNN) have demonstrated promising performance. To fully explore both visual features in a unified framework for effective and efficient retrieval, we propose a collaborative index embedding method to implicitly integrate the index matrices of them. We formulate the index embedding as an optimization problem from the perspective of neighborhood sharing and solve it with an alternating index update scheme. After the iterative embedding, only the embedded CNN index is kept for on-line query, which demonstrates significant gain in retrieval accuracy, with very economical memory cost...
March 1, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28278456/boosted-random-ferns-for-object-detection
#15
Michael Villamizar, Juan Andrade-Cetto, Alberto Sanfeliu, Francesc Moreno-Noguer
In this paper we introduce the Boosted Random Ferns (BRFs) to rapidly build discriminative classifiers for learning and detecting object categories. At the core of our approach we use standard random ferns, but we introduce four main innovations that let us bring ferns from an instance to a category level, and still retain efficiency. First, we define binary features on the histogram of oriented gradients-domain (as opposed to intensity-), allowing for a better representation of intra-class variability. Second, both the positions where ferns are evaluated within the sliding window, and the location of the binary features for each fern are not chosen completely at random, but instead we use a boosting strategy to pick the most discriminative combination of them...
March 1, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28252390/template-matching-via-densities-on-the-roto-translation-group
#16
Erik Bekkers, Marco Loog, Bart Ter Haar Romeny, Remco Duits
We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns. Our method is based on data representations via orientation scores, which are functions on the space of positions and orientations, and which are obtained via a wavelet-type transform. This new representation allows us to detect orientation patterns in an intuitive and direct way, namely via cross-correlations. Additionally, we propose a generalized linear regression framework for the construction of suitable templates using smoothing splines...
February 24, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28237921/curvilinear-structure-analysis-by-ranking-the-orientation-responses-of-path-operators
#17
Odyssee Merveille, Hugues Talbot, Laurent Najman, Nicolas Passat
The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, nonlinear operator, called RORPO (Ranking Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local...
February 22, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28237920/separability-oriented-subclass-discriminant-analysis
#18
Huan Wan, Hui Wang, Gongde Guo, Xin Wei
Linear discriminant analysis (LDA) is a classical method for discriminative dimensionality reduction. The original LDA may degrade in its performance for non-Gaussian data, and may be unable to extract sufficient features to satisfactorily explain the data when the number of classes is small. Two prominent extensions to address these problems are subclass discriminant analysis (SDA) and mixture subclass discriminant analysis (MSDA). They divide every class into subclasses and re-define the within-class and between-class scatter matrices on the basis of subclass...
February 22, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28221993/shading-based-surface-detail-recovery-under-general-unknown-illumination
#19
Di Xu, Qi Duan, Jianmin Zheng, Juyong Zhang, Jianfei Cai, Tat-Jen Cham
Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision and high quality reconstruction is usually challenging especially when fine detail is needed and the albedo of the object is non-uniform. This paper introduces vertex overall illumination vectors to model the illumination effect and presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are the two important observations: (1) the illumination over the surface of an object often appears to be piece wise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing the surface, which was often overlooked previously...
February 17, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28221992/exploiting-feature-and-class-relationships-in-video-categorization-with-regularized-deep-neural-networks
#20
Yu-Gang Jiang, Zuxuan Wu, Jun Wang, Xiangyang Xue, Shih-Fu Chang
In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance...
February 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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