pwc-archive/papers-with-abstracts huggingface dataset:
Heteroscedastic Max-Min Distance Analysis
Authors: Bing Su, Xiaoqing Ding, Changsong Liu, Ying Wu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dimensionality Reduction

Many discriminant analysis methods such as LDA and HLDA actually maximize the average pairwise distances between classes, which often causes the class separation problem. Max-min distance analysis (MMDA) addresses this problem by maximizing the minimum pairwise distance in the latent subspace, but it is developed under the homoscedastic assumption. This paper proposes Heteroscedastic MMDA (HMMDA) methods that explore the discriminative information in the difference of intra-class scatters for dimensionality reduction. WHMMDA maximizes the minimal pairwise Chenoff distance in the whitened space. OHMMDA incorporates this objective and the minimization of class compactness into a trace quotient formulation and imposes an orthogonal constraint to the final transformation, which can be solved by a bisection search algorithm. Two variants of OHMMDA are further proposed to encode the margin information. Experiments on several UCI Machine Learning datasets and the Yale Face database demonstrate the effectiveness of the proposed HMMDA methods.
Sparse Composite Quantization
Authors: Ting Zhang, Guo-Jun Qi, Jinhui Tang, Jingdong Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Quantization, Retrieval

The quantization techniques have shown competitive performance in approximate nearest neighbor search. The state-of-the-art algorithm, composite quantization, takes advantage of the compositionabity, i.e., the vector approximation accuracy, as opposed to product quantization and Cartesian k-means. However, we have observed that the runtime cost of computing the distance table in composite quantization, which is used as a lookup table for fast distance computation, becomes nonnegligible in real applications, e.g., reordering the candidates retrieved from the inverted index when handling very large scale databases. To address this problem, we develop a novel approach, called sparse composite quantization, which constructs sparse dictionaries. The benefit is that the distance evaluation between the query and the dictionary element (a sparse vector) is accelerated using the efficient sparse vector operation, and thus the cost of distance table computation is reduced a lot. Experiment results on large scale ANN retrieval tasks (1M SIFTs and 1B SIFTs) and applications to object retrieval show that the proposed approach yields competitive performance: superior search accuracy to product quantization and Cartesian k-means with almost the same computing cost, and much faster ANN search than composite quantization with the same level of accuracy.
Sparse Representation Classification With Manifold Constraints Transfer
Authors: Baochang Zhang, Alessandro Perina, Vittorio Murino, Alessio Del Bue
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification, Sparse Representation-based Classification

The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems. In this paper we leverage the specific structure of data in order to improve recognition accuracies in general recognition tasks. In particular we propose a novel framework that allows to embed manifold priors into sparse representation-based classification (SRC) approaches. We also show that manifold constraints can be transferred from the data to the optimized variables if these are linearly correlated. Using this new insight, we define an efficient alternating direction method of multipliers (ADMM) that can consistently integrate the manifold constraints during the optimization process. This is based on the property that we can recast the problem as the projection over the manifold via a linear embedding method based on the Geodesic distance. The proposed approach is successfully applied on face, digit, action and objects recognition showing a consistently increase on performance when compared to the state of the art.
Photometric Stereo With Near Point Lighting: A Solution by Mesh Deformation
Authors: Wuyuan Xie, Chengkai Dai, Charlie C. L. Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We tackle the problem of photometric stereo under near point lighting in this paper. Different from the conventional formulation of photometric stereo that assumes parallel lighting, photometric stereo under the near point lighting condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. To solve this non-linear problem of PS with near point lighting, a local/global mesh deformation approach is developed in our work to determine the position and the orientation of a facet simultaneously, where each facet is corresponding to a pixel in the image captured by the camera. Unlike nonlinear optimization schemes, the mesh deformation in our approach is decoupled into an iteration of interlaced steps of local projection and global blending. Experimental results verify that our method can generate accurate estimation of surface shape under near point lighting in a few iterations. Besides, this approach is robust to errors on the positions of light sources and is easy to be implemented.
Efficient Label Collection for Unlabeled Image Datasets
Authors: Maggie Wigness, Bruce A. Draper, J. Ross Beveridge
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Active Learning, Autonomous Navigation, Clustering, Scene Understanding

Visual classifiers are part of many applications including surveillance, autonomous navigation and scene understanding. The raw data used to train these classifiers is abundant and easy to collect but lacks labels. Labels are necessary for training supervised classifiers, but the labeling process requires significant human effort. Techniques like active learning and group-based labeling have emerged to help reduce the labeling workload. However, the possibility of collecting label noise affects either the efficiency of these systems or the performance of the trained classifiers. Further, many introduce latency by iteratively re-training classifiers or re-clustering data. We introduce a technique that searches for structural change in hierarchically clustered data to identify a set of clusters that span a spectrum of visual concept granularities. This allows us to efficiently label clusters with less label noise and produce high performing classifiers. The data is hierarchically clustered only once, eliminating latency during the labeling process. Using benchmark data we show that collecting labels with our approach is more efficient than existing labeling techniques, and achieves higher classification accuracy. Finally, we demonstrate the speed and efficiency of our system using real-world data collected for an autonomous navigation task.
Separating Objects and Clutter in Indoor Scenes
Authors: Salman H. Khan, Xuming He, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes. We propose to solve both of these problems in a joint framework using RGBD images of indoor scenes. In contrast to recent approaches which focus on either one of these two problems, we perform `fine grained structure categorization' by predicting all the major objects and simultaneously labeling the cluttered regions. A conditional random field model is proposed to incorporate a rich set of local appearance, geometric features and interactions between the scene elements. We take a structural learning approach with a loss of 3D localisation to estimate the model parameters from a large annotated RGBD dataset, and a mixed integer linear programming formulation for inference. We demonstrate that our approach is able to detect cuboids and estimate cluttered regions across many different object and scene categories in the presence of occlusion, illumination and appearance variations.
FaLRR: A Fast Low Rank Representation Solver
Authors: Shijie Xiao, Wen Li, Dong Xu, DaCheng Tao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Face Clustering

Low rank representation (LRR) has shown promising performance for various computer vision applications such as face clustering. Existing algorithms for solving LRR usually depend on its two-variable formulation which contains the original data matrix. In this paper, we develop a fast LRR solver called FaLRR, by reformulating LRR as a new optimization problem with regard to factorized data (which is obtained by skinny SVD of the original data matrix). The new formulation benefits the corresponding optimization and theoretical analysis. Specifically, to solve the resultant optimization problem, we propose a new algorithm which is not only efficient but also theoretically guaranteed to obtain a globally optimal solution. Regarding the theoretical analysis, the new formulation is helpful for deriving some interesting properties of LRR. Last but not least, the proposed algorithm can be readily incorporated into an existing distributed framework of LRR for further acceleration. Extensive experiments on synthetic and real-world datasets demonstrate that our FaLRR achieves order-of-magnitude speedup over existing LRR solvers, and the efficiency can be further improved by incorporating our algorithm into the distributed framework of LRR.
Simulating Makeup Through Physics-Based Manipulation of Intrinsic Image Layers
Authors: Chen Li, Kun Zhou, Stephen Lin
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present a method for simulating makeup in a face image. To generate realistic results without detailed geometric and reflectance measurements of the user, we propose to separate the image into intrinsic image layers and alter them according to proposed adaptations of physically-based reflectance models. Through this layer manipulation, the measured properties of cosmetic products are applied while preserving the appearance characteristics and lighting conditions of the target face. This approach is demonstrated on various forms of cosmetics including foundation, blush, lipstick, and eye shadow. Experimental results exhibit a close approximation to ground truth images, without artifacts such as transferred personal features and lighting effects that degrade the results of image-based makeup transfer methods.
Shape-Based Automatic Detection of a Large Number of 3D Facial Landmarks
Authors: Syed Zulqarnain Gilani, Faisal Shafait, Ajmal Mian
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present an algorithm for automatic detection of a large number of anthropometric landmarks on 3D faces. Our approach does not use texture and is completely shape based in order to detect landmarks that are morphologically significant. The proposed algorithm evolves level set curves with adaptive geometric speed functions to automatically extract effective seed points for dense correspondence. Correspondences are established by minimizing the bending energy between patches around seed points of given faces to those of a reference face. Given its hierarchical structure, our algorithm is capable of establishing thousands of correspondences between a large number of faces. Finally, a morphable model based on the dense corresponding points is fitted to an unseen query face for transfer of correspondences and hence automatic detection of landmarks. The proposed algorithm can detect any number of pre-defined landmarks including subtle landmarks that are even difficult to detect manually. Extensive experimental comparison on two benchmark databases containing 6,507 scans shows that our algorithm outperforms six state of the art algorithms.
Material Classification With Thermal Imagery
Authors: Philip Saponaro, Scott Sorensen, Abhishek Kolagunda, Chandra Kambhamettu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, Diversity, General Classification, Material Classification

Material classification is an important area of research in computer vision. Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversity of colors in a single material class. In this work we study the use of long wave infrared (i.e. thermal) imagery for material classification. Thermal imagery has the benefit of relative invariance to color changes,  invariance to lighting conditions, and can even work in the dark. We collect a database of 21 different material classes with both color and thermal imagery. We develop a set of features that describe water permeation and heating/cooling properties, and test several variations on these methods to obtain our final classifier. The results show that the proposed method outperforms typical color and texture features, and when combined with color information, the results are improved further.
Deeply Learned Attributes for Crowded Scene Understanding
Authors: Jing Shao, Kai Kang, Chen Change Loy, Xiaogang Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Attribute, Multi-Task Learning, Scene Understanding

Crowded scene understanding is a fundamental problem in computer vision. In this study, we develop a multi-task deep model to jointly learn and combine appearance and motion features for crowd understanding. We propose crowd motion channels as the input of the deep model and the channel design is inspired by generic properties of crowd systems. To well demonstrate our deep model, we construct a new large-scale WWW Crowd dataset with 10000 videos from 8257 crowded scenes, and build an attribute set with 94 attributes on WWW. We further measure user study performance on WWW and compare this with the proposed deep models. Extensive experiments show that our deep models display significant performance improvements in cross-scene attribute recognition compared to strong crowd-related feature-based baselines, and the deeply learned features behave an superior performance in multi-task learning.
Learning To Look Up: Realtime Monocular Gaze Correction Using Machine Learning
Authors: Daniil Kononenko, Victor Lempitsky
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: BIG-bench Machine Learning

We revisit the well-known problem of gaze correction and present a solution based on supervised machine learning. At training time, our system observes pairs of images, where each pair contains the face of the same person with a fixed angular difference in gaze direction. It then learns to synthesize the second image of a pair from the first one. After learning, the system becomes able to redirect the gaze of a previously unseen person by the same angular difference. Unlike many previous solutions to gaze problem in videoconferencing, ours is purely monocular, i.e. it does not require any hardware apart from an in-built web-camera of a laptop.  We base our machine learning implementation on a special kind of decision forests that predict a displacement (flow) vector for each pixel in the input image. As a result, our system is highly efficient (runs in real-time on a single core of a modern laptop). In the paper, we demonstrate results on a variety of videoconferencing frames and evaluate the method quantitatively on the hold-out set of registered images. The supplementary video shows example sessions of our system at work.
Real-Time 3D Head Pose and Facial Landmark Estimation From Depth Images Using Triangular Surface Patch Features
Authors: Chavdar Papazov, Tim K. Marks, Michael Jones
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Alignment, Head Pose Estimation, Pose Estimation

We present a real-time system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. We introduce a novel triangular surface patch (TSP) descriptor, which encodes the shape of the 3D surface of the face within a triangular area. The proposed descriptor is viewpoint invariant, and it is robust to noise and to variations in the data resolution. Using a fast nearest neighbor lookup, TSP descriptors from an input depth map are matched to the most similar ones that were computed from synthetic head models in a training phase. The matched triangular surface patches in the training set are used to compute estimates of the 3D head pose and facial landmark positions in the input depth map. By sampling many TSP descriptors, many votes for pose and landmark positions are generated which together yield robust final estimates.  We evaluate our approach on the publicly available Biwi Kinect Head Pose Database to compare it against state-of-the-art methods. Our results show a significant improvement in the accuracy of both pose and landmark location estimates while maintaining real-time speed.
Aligning 3D Models to RGB-D Images of Cluttered Scenes
Authors: Saurabh Gupta, Pablo Arbelaez, Ross Girshick, Jitendra Malik
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art, while being an order of magnitude faster.
Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold
Authors: Yan Li, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval, Video Retrieval

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching two heterogeneous representations, respectively in Euclidean space and Riemannian manifold. This work makes the first attempt to embed the two heterogeneous spaces into a common discriminant Hamming space. Specifically, we propose Hashing across Euclidean space and Riemannian manifold (HER) by deriving a unified framework to firstly embed the two spaces into corresponding reproducing kernel Hilbert spaces, and then iteratively optimize the intra- and inter-space Hamming distances in a maxmargin framework to learn the hash functions for the two spaces. Extensive experiments demonstrate the impressive superiority of our method over the state-of-the-art competitive hash learning methods.
Social Saliency Prediction
Authors: Hyun Soo Park, Jianbo Shi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Prediction, Saliency Prediction

This paper presents a method to predict social saliency, the likelihood of joint attention, given an input image or video by leveraging the social interaction data captured by first person cameras. Inspired by electric dipole moments, we introduce a social formation feature that encodes the geometric relationship between joint attention and its social formation. We learn this feature from the first person social interaction data where we can precisely measure the locations of joint attention and its associated members in 3D. An ensemble classifier is trained to learn the geometric relationship. Using the trained classifier, we predict social saliency in real-world scenes with multiple social groups including scenes from team sports captured in a third person view. Our representation does not require directional measurements such as gaze directions. A geometric analysis of social interactions in terms of the F-formation theory is also presented.
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling
Authors: Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Age Estimation, Face Recognition, Image Reconstruction, Super-Resolution

The "interpretation through synthesis", i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at "explaining" face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of human face images, e.g. facial poses, occlusions, lighting, low resolution, etc. Since these variations are mostly non-linear, it is impossible to represent them in a linear model, such as Principal Component Analysis (PCA). This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations. In this approach, three crucial components represented in hierarchical layers are modeled using the Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAMs are therefore superior to AAMs in inferring a representation for new face images under various challenging conditions. In addition, DAMs have ability to generate a compact set of parameters in higher level representation that can be used for classification, e.g. face recognition and facial age estimation. The proposed approach is evaluated in facial image reconstruction, facial super-resolution on two databases, i.e. LFPW and Helen. It is also evaluated on FG-NET database for the problem of age estimation.
Statistical Inference Models for Image Datasets With Systematic Variations
Authors: Won Hwa Kim, Barbara B. Bendlin, Moo. K. Chung, Sterling C. Johnson, Vikas Singh
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience. However, when there are systematic variations in the images due to parameters changes such as changes in the scanner protocol, hardware changes, or when combining data from multi-site studies, the statistical analysis becomes problematic. Motivated by this scenario, the goal of this paper is to develop a unified statistical solution to the problem of systematic variations in statistical image analysis. Based in part on recent literature in harmonic analysis on diffusion maps, we propose an algorithm which compares operators that are resilient to the systematic variations described above. These operators are derived from the empirical measurements of the image data and provide an efficient surrogate to capturing the actual changes across images. We also establish a connection between our method to the design of Wavelets in non-Euclidean space. To evaluate the proposed ideas, we present various experimental results on detecting changes in simulations as well as show how the method offers improved statistical power in the analysis of longitudinal real PIB-PET imaging data acquired from participants at risk for Alzheimer's disease(AD).