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IEEE Transactions on Pattern Analysis and Machine Intelligence

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https://www.readbyqxmd.com/read/29053445/partition-level-constrained-clustering
#1
Hongfu Liu, Zhiqiang Tao, Yun Fu
Constrained clustering uses pre-given knowledge to improve the clustering performance. Here we use a new constraint called partition level side information and propose the Partition Level Constrained Clustering (PLCC) framework' where only a small proportion of the data is given labels to guide the procedure of clustering. Our goal is to find a partition which captures the intrinsic structure from the data itself, and also agrees with the partition level side information. Then we derive the algorithm of partition level side information based on K-means and give its corresponding solution...
October 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/29035211/error-correcting-factorization
#2
Miguel Angel Bautista, Oriol Pujol, Fernando de la Torre, Sergio Escalera
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pairwise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method...
October 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/29035210/inextensible-non-rigid-structure-from-motion-by-second-order-cone-programming
#3
Ajad Chhatkuli, Daniel Pizarro, Toby Collins, Adrien Bartoli
We present a global and convex formulation for the templateless 3D reconstruction of a deforming object with the perspective camera. We show for the first time how to construct a Second-Order Cone Programming (SOCP) problem for Non-Rigid Structure-from-Motion (NRSfM) using the Maximum-Depth Heuristic (MDH). In this regard, we deviate strongly from the general trend of using affine cameras and factorization-based methods to solve NRSfM, which do not perform well with complex nonlinear deformations. In MDH, the points' depths are maximized so that the distance between neighbouring points in camera space are upper bounded by the geodesic distance...
October 13, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/29028188/zero-shot-learning-using-synthesised-unseen-visual-data-with-diffusion-regularisation
#4
Yang Long, Li Liu, Fumin Shen, Ling Shao, Xuelong Li
Sufficient training examples are the fundamental requirement for most of the learning tasks. However, collecting welllabelled training examples is costly. Inspired by Zero-shot Learning (ZSL) that can make use of visual attributes or natural language semantics as an intermediate level clue to associate low-level features with high-level classes, in a novel extension of this idea, we aim to synthesise training data for novel classes using only semantic attributes. Despite the simplicity of this idea, there are several challenges...
October 12, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/29028187/dynamic-video-deblurring-using-a-locally-adaptive-linear-blur-model
#5
Tae Hyun Kim, Seungjun Nah, Kyoung Mu Lee
State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels...
October 10, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28991734/characterization-of-color-images-with-multiscale-monogenic-maxima
#6
Raphael Soulard, Philippe Carre
Can we build a feature-based analysis that fully characterizes images? The literature answers with edge-based reconstruction methods inspired by Marr's paradigm but limited to the greyscale case. This paper studies the color case.
October 6, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28991733/isometric-non-rigid-shape-from-motion-with-riemannian-geometry-solved-in-linear-time
#7
Shaifali Parashar, Daniel Pizarro, Adrien Bartoli
We study Isometric Non-Rigid Shape-from-Motion (Iso-NRSfM): given multiple intrinsically calibrated monocular images, we want to reconstruct the time-varying 3D shape of a thin-shell object undergoing isometric deformations. We show that Iso-NRSfM is solvable from local warps, the inter-image geometric transformations. We propose a new theoretical framework based on the Riemmanian manifold to represent the unknown 3D surfaces as embeddings of the camera's retinal plane. This allows us to use the manifold's metric tensor and Christoffel Symbol (CS) fields...
October 6, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28981406/the-kendall-and-mallows-kernels-for-permutations
#8
Yunlong Jiao, Jean-Philippe Vert
We show that the widely used Kendall tau correlation coefficient, and the related Mallows kernel, are positive definite kernels for permutations. They offer computationally attractive alternatives to more complex kernels on the symmetric group to learn from rankings, or learn to rank. We show how to extend these kernels to partial rankings, multivariate rankings and uncertain rankings. Examples are presented on how to formulate typical problems of learning from rankings such that they can be solved with state-of-the-art kernel algorithms...
October 5, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28976312/object-segmentation-ensuring-consistency-across-multi-viewpoint-images
#9
Seunghwa Jeong, Jungjin Lee, Bumki Kim, Younghui Kim, Junyong Noh
We present a hybrid approach that segments an object by using both color and depth information obtained from views captured from a low-cost RGBD camera and sparsely-located color cameras. Our system begins with generating dense depth information of each target image by using Structure from Motion and joint bilateral upsampling. We formulate the multi-view object segmentation as the Markov Random Field energy optimization on the graph constructed from the superpixels. To ensure inter-view consistency of the segmentation results between color images that have too few color features, our local mapping method generates dense inter-view geometric correspondences by using the dense depth images...
September 29, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28976311/face-recognition-via-collaborative-representation-its-discriminant-nature-and-superposed-representation
#10
Weihong Deng, Jiani Hu, Jun Guo
Collaborative representation methods, such as sparse subspace clustering (SSC) and sparse representation-based classification (SRC), have achieved great success in face clustering and classification by directly utilizing the training images as the dictionary bases. In this paper, we reveal that the superior performance of collaborative representation relies heavily on the sufficiently large class separability of the controlled face datasets such as Extended Yale B. On the uncontrolled or undersampled dataset, however, collaborative representation suffers from the misleading coefficients of the incorrect classes...
September 29, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28961104/ghost-numbers
#11
Liang Chen, David Casperson, Lixin Gao
We comment on a paper describing an algorithm for image set classification. Following the general practice in computer vision research, the performance of the algorithm was evaluated on benchmarks in order to support the claim of its advantage over other algorithms in the literature. We have examined the reported data of experiences on two datasets, and found that many numbers are not a possible answer regardless how the random partitions were selected and regardless how the algorithms performed in each partition...
September 28, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28961103/s-cnn-subcategory-aware-convolutional-networks-for-object-detection
#12
Tao Chen, Shijian Lu, Jiayuan Fan
The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process...
September 26, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28961102/discriminative-multiple-instance-hyperspectral-target-characterization
#13
Alina Zare, Changzhe Jiao, Taylor Glenn
In this paper, two methods for discriminative multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications, such as sub-pixel target detection in remotely-sensed hyperspectral imagery, accurate pixel-level labels on training data is often unavailable and infeasible to obtain. Furthermore, since sub-pixel targets are smaller in size than the resolution of a single pixel, training data is comprised only of mixed data points (in which target training points are mixtures of responses from both target and non-target classes)...
September 26, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28952936/dual-sticky-hierarchical-dirichlet-process-hidden-markov-model-and-its-application-to-natural-language-description-of-motions
#14
Weiming Hu, Guodong Tian, Yongxin Kang, Chunfeng Yuan, Stephen Maybank
In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions...
September 25, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28952935/deblurring-images-via-dark-channel-prior
#15
Jinshan Pan, Deqing Sun, Hanspeter Pfister, Ming-Hsuan Yang
We present an effective blind image deblurring algorithm based on the dark channel prior. The motivation of this work is an interesting observation that the dark channel of blurred images is less sparse. While most patches in a clean image contain some dark pixels, this is not the case when they are averaged with neighboring ones by motion blur. This change in sparsity of the dark channel pixels is an inherent property of the motion blur process, which we prove mathematically and validate using image data. Enforcing sparsity of the dark channel thus helps blind deblurring in various scenarios such as natural, face, text, and low-illumination images...
September 22, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28952934/bayesian-helmholtz-stereopsis-with-integrability-prior
#16
Nadejda Roubtsova, Jean-Yves Guillemaut
Helmholtz Stereopsis is a 3D reconstruction method uniquely independent of surface reflectance. Yet, its sub-optimal maximum likelihood formulation with drift-prone normal integration limits performance. Via three contributions this paper presents a complete novel pipeline for Helmholtz Stereopsis. Firstly, we propose a Bayesian formulation replacing the maximum likelihood problem by a maximum a posteriori one. Secondly, a tailored prior enforcing consistency between depth and normal estimates via a novel metric related to optimal surface integrability is proposed...
September 22, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28945589/structure-aware-data-consolidation
#17
Shihao Wu, Peter Bertholet, Hui Huang, Daniel Cohen-Or, Minglun Gong, Matthias Zwicker
We present a structure-aware technique to consolidate noisy data, which we use as a pre-process for standard clustering and dimensionality reduction. Our technique is related to mean shift, but instead of seeking density modes, it reveals and consolidates continuous high density structures such as curves and surface sheets in the underlying data while ignoring noise and outliers. We provide a theoretical analysis under some assumptions, and show that our approach significantly improves the performance of many non-linear dimensionality reduction and clustering algorithms in challenging scenarios...
September 19, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28945588/fvqa-fact-based-visual-question-answering
#18
Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, Anton van den Hengel
Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example...
September 19, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28922115/colour-constancy-beyond-the-classical-receptive-field
#19
Arash Akbarinia, C Alejandro Parraga
The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain...
September 18, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28922114/cross-modal-scene-networks
#20
Yusuf Aytar, Lluis Castrejon, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality...
September 18, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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