pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Squared Curvature
Authors: Claudia Nieuwenhuis, Eno Toeppe, Lena Gorelick, Olga Veksler, Yuri Boykov
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Curvature has received increasing attention as an important alternative to length based regularization in computer vision. In contrast to length, it preserves elongated structures and fine details. Existing approaches are either inefficient, or have low angular resolution and yield results with strong block artifacts. We derive a new model for computing squared curvature based on integral geometry. The model counts responses of straight line triple cliques. The corresponding energy decomposes into submodular and supermodular pairwise potentials.  We show that this energy can be efficiently minimized even for high angular resolutions using the trust region framework. Our results confirm that we obtain accurate and visually pleasing solutions without strong artifacts at reasonable runtimes.
Multi-feature Spectral Clustering with Minimax Optimization
Authors: Hongxing Wang, Chaoqun Weng, Junsong Yuan
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering

In this paper, we propose a novel formulation for multi-feature clustering using minimax optimization. To find a consensus clustering result that is agreeable to all feature modalities, our objective is to find a universal feature embedding, which not only fits each individual feature modality well, but also unifies different feature modalities by minimizing their pairwise disagreements. The loss function consists of both (1) unary embedding cost for each modality, and (2) pairwise disagreement cost for each pair of modalities, with weighting parameters automatically selected to maximize the loss. By performing minimax optimization, we can minimize the loss for the worst case with maximum disagreements, thus can better reconcile different feature modalities. To solve the minimax optimization, an iterative solution is proposed to update the universal embedding, individual embedding, and fusion weights, separately. Our minimax optimization has only one global parameter. The superior results on various multi-feature clustering tasks validate the effectiveness of our approach when compared with the state-of-the-art methods.
Newton Greedy Pursuit: A Quadratic Approximation Method for Sparsity-Constrained Optimization
Authors: Xiao-Tong Yuan, Qingshan Liu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: parameter estimation

First-order greedy selection algorithms have been widely applied to sparsity-constrained optimization. The main theme of this type of methods is to evaluate the function gradient in the previous iteration to update the non-zero entries and their values in the next iteration. In contrast, relatively less effort has been made to study the second-order greedy selection method additionally utilizing the Hessian information. Inspired by the classic constrained Newton method, we propose in this paper the NewTon Greedy Pursuit (NTGP) method to approximately minimizes a twice differentiable function over sparsity constraint. At each iteration, NTGP constructs a second-order Taylor expansion to approximate the cost function, and estimates the next iterate as the solution of the constructed quadratic model over sparsity constraint. Parameter estimation error and convergence property of NTGP are analyzed. The superiority of NTGP to several representative first-order greedy selection methods is demonstrated in synthetic and real sparse logistic regression tasks.
Latent Dictionary Learning for Sparse Representation based Classification
Authors: Meng Yang, Dengxin Dai, Lilin Shen, Luc van Gool
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, Dictionary Learning, Face Recognition, General Classification, Open-Ended Question Answering, Sparse Representation-based Classification

Dictionary  learning (DL) for  sparse  coding  has  shown promising  results  in classification  tasks, while how  to  adaptively build the relationship between dictionary atoms  and  class  labels  is  still  an  important open question. The existing  dictionary  learning  approaches simply fix  a dictionary atom to be either class-specific or shared by all classes beforehand, ignoring that the relationship needs to be updated during DL. To address this issue, in this paper we  propose  a  novel latent  dictionary  learning  (LDL) method  to learn a  discriminative  dictionary and  build  its relationship to  class  labels  adaptively.  Each  dictionary atom  is  jointly  learned  with  a  latent  vector,  which associates  this  atom  to the  representation  of  different classes.  More  specifically,  we introduce  a  latent representation  model,  in  which  discrimination  of  the learned  dictionary  is  exploited  via  minimizing  the  within-class  scatter  of  coding  coefficients  and  the  latent-value  weighted  dictionary  coherence. The  optimal  solution  is  efficiently  obtained  by  the  proposed  solving algorithm. Correspondingly, a latent sparse representation based  classifier  is  also  presented.  Experimental  results demonstrate that our algorithm outperforms many recently proposed  sparse representation  and dictionary learning approaches for action, gender and face recognition.
Is Rotation a Nuisance in Shape Recognition?
Authors: Qiuhong Ke, Yi Li
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Rotation in closed contour recognition is a puzzling nuisance in most algorithms. In this paper we address three fundamental issues brought by rotation in shapes: 1) is alignment among shapes necessary? If the answer is "no", 2) how to exploit information in different rotations? and 3) how to use rotation unaware local features for rotation aware shape recognition?  We argue that the origin of these issues is the use of hand crafted rotation-unfriendly features and measurements. Therefore our goal is to learn a set of hierarchical features that describe all rotated versions of a shape as a class, with the capability of distinguishing different such classes. We propose to rotate shapes as many times as possible as training samples, and learn the hierarchical feature representation by effectively adopting a convolutional neural network. We further show that our method is very efficient because the network responses of all possible shifted versions of the same shape can be computed effectively by re-using information in the overlapping areas. We tested the algorithm on three real datasets: Swedish Leaves dataset, ETH-80 Shape, and a subset of the recently collected Leafsnap dataset. Our approach used the curvature scale space and outperformed the state of the art.
Dual-Space Decomposition of 2D Complex Shapes
Authors: Guilin Liu, Zhonghua Xi, Jyh-Ming Lien
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

While techniques that segment shapes into visually meaningful parts have generated impressive results, these techniques also have only focused on relatively simple shapes, such as those composed of a single object either without holes or with few simple holes. In many applications, shapes created from images can contain many overlapping objects and holes. These holes may come from sensor noise, may have important parts of the shape or may be arbitrarily complex. These complexities that appear in real-world 2D shapes can pose grand challenges to the existing part segmentation methods. In this paper, we propose a new decomposition method, called Dual-space Decomposition that handles complex 2D shapes by recognizing the importance of holes and classifying holes as either topological noise or structurally important features. Our method creates a nearly convex decomposition of a given shape by segmenting both the polygon itself and its complementary. We compare our results to segmentation produced by nonexpert human subjects. Based on two evaluation methods, we show that this new decomposition method creates statistically similar to those produced by human subjects.
Noising versus Smoothing for Vertex Identification in Unknown Shapes
Authors: Konstantinos A. Raftopoulos, Marin Ferecatu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

A method for identifying shape features of local nature on the shape's boundary, in a way that is facilitated by the presence of noise is presented. The boundary is seen as a real function. A study of a certain distance function reveals, almost counter-intuitively, that vertices can be defined and localized better in the presence of noise, thus the concept of noising, as opposed to smoothing, is conceived and presented. The method works on both smooth and noisy shapes, the presence of noise having an effect of improving on the results of the smoothed version. Experiments with noise and a comparison to state of the art validate the method.
Surface Registration by Optimization in Constrained Diffeomorphism Space
Authors: Wei Zeng, Lok Ming Lui, Xianfeng GU
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

This work proposes a novel framework for optimization in the constrained diffeomorphism space for deformable surface registration. First the diffeomorphism space is modeled as a special complex functional space on the source surface, the Beltrami coefficient space. The physically plausible constraints, in terms of feature landmarks and deformation types, define subspaces in the Beltrami coefficient space. Then the harmonic energy of the registration is minimized in the constrained subspaces. The minimization is achieved by alternating two steps: 1) optimization - diffuse the Beltrami coefficient, and 2) projection - first deform the conformal structure by the current Beltrami coefficient and then compose with a harmonic map from the deformed conformal structure to the target. The registration result is diffeomorphic, satisfies the physical landmark and deformation constraints, and minimizes the conformality distortion. Experiments on human facial surfaces demonstrate the efficiency and efficacy of the proposed registration framework.
Dense Non-Rigid Shape Correspondence using Random Forests
Authors: Emanuele Rodola, Samuel Rota Bulo, Thomas Windheuser, Matthias Vestner, Daniel Cremers
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations. In a scenario where this class is represented by a small set of example shapes, the proposed method learns a shape descriptor capturing the variability of the deformations in the given class. The approach enables the wave kernel signature to extend the class of recognized deformations from near isometries to the deformations appearing in the example set by means of a random forest classifier. With the help of the introduced spatial regularization, the proposed method achieves significant improvements over the baseline approach and obtains state-of-the-art results while keeping short computation times.
Covariance Descriptors for 3D Shape Matching and Retrieval
Authors: Hedi Tabia, Hamid Laga, David Picard, Philippe-Henri Gosselin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, Retrieval

Several descriptors have been proposed in the past for 3D shape analysis, yet none of them achieves best performance on all shape classes. In this paper we propose a novel method for 3D shape analysis using the covariance matrices of the descriptors rather than the descriptors themselves. Covariance matrices enable efficient fusion of different types of features and modalities. They capture, using the same representation, not only the geometric and the spatial properties of a shape region but also the correlation of these properties within the region. Covariance matrices, however, lie on the manifold of Symmetric Positive Definite (SPD) tensors, a special type of Riemannian manifolds, which makes comparison and clustering of such matrices challenging. In this paper we study covariance matrices in their native space and make use of geodesic distances on the manifold as a dissimilarity measure. We demonstrate the performance of this metric on 3D face matching and recognition tasks. We then generalize the Bag of Features paradigm, originally designed in Euclidean spaces, to the Riemannian manifold of SPD matrices. We propose a new clustering procedure that takes into account the geometry of the Riemannian manifold. We evaluate the performance of the proposed Bag of Covariance Matrices framework on 3D shape matching and retrieval applications and demonstrate its superiority compared to descriptor-based techniques.
Symmetry-Aware Nonrigid Matching of Incomplete 3D Surfaces
Authors: Yusuke Yoshiyasu, Eiichi Yoshida, Kazuhito Yokoi, Ryusuke Sagawa
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Graph Matching

We present a nonrigid shape matching technique for establishing correspondences of incomplete 3D surfaces that exhibit intrinsic reflectional symmetry. The key for solving the symmetry ambiguity problem is to use a point-wise local mesh descriptor that has orientation and is thus sensitive to local reflectional symmetry, e.g. discriminating the left hand and the right hand. We devise a way to compute the descriptor orientation by taking the gradients of a scalar field  called the average diffusion distance (ADD). Because ADD is smoothly defined on a surface, invariant under isometry/scale and robust to topological errors, the robustness of the descriptor to non-rigid deformations is improved. In addition, we propose a graph matching algorithm called iterative spectral relaxation which combines spectral embedding and spectral graph matching. This formulation allows us to define pairwise constraints in a scale-invariant manner from k-nearest neighbor local pairs such that non-isometric deformations can be  robustly handled. Experimental results show that our method can match challenging surfaces with global intrinsic symmetry, data incompleteness and non-isometric deformations.
An Automated Estimator of Image Visual Realism Based on Human Cognition
Authors: Shaojing Fan, Tian-Tsong Ng, Jonathan S. Herberg, Bryan L. Koenig, Cheston Y.-C. Tan, Rangding Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Assessing the visual realism of images is increasingly becoming an essential aspect of fields ranging from computer graphics (CG) rendering to photo manipulation. In this paper we systematically evaluate factors underlying human perception of visual realism and use that information to create an automated assessment of visual realism. We make the following unique contributions. First, we established a benchmark dataset of images with empirically determined visual realism scores. Second, we identified attributes potentially related to image realism, and used correlational techniques to determine that realism was most related to image naturalness, familiarity, aesthetics, and semantics. Third, we created an attributes-motivated, automated computational model that estimated image visual realism quantitatively. Using human assessment as a benchmark, the model was below human performance, but outperformed other state-of-the-art algorithms.
SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization
Authors: Shuaicheng Liu, Lu Yuan, Ping Tan, Jian Sun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Optical Flow Estimation, Video Stabilization

We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization. A SteadyFlow is a specific optical flow by enforcing strong spatial coherence, such that smoothing feature trajectories can be replaced by smoothing pixel profiles, which are motion vectors collected at the same pixel location in the SteadyFlow over time. In this way, we can avoid brittle feature tracking in a video stabilization system. Besides, SteadyFlow is a more general 2D motion model which can deal with spatially-variant motion. We initialize the SteadyFlow by optical flow and then discard discontinuous motions by a spatial-temporal analysis and fill in missing regions by motion completion. Our experiments demonstrate the effectiveness of our stabilization on real-world challenging videos.
Joint Summarization of Large-scale Collections of Web Images and Videos for Storyline Reconstruction
Authors: Gunhee Kim, Leonid Sigal, Eric P. Xing
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 16k, Diversity, Video Summarization

In this paper, we address the problem of jointly summarizing large sets of Flickr images and YouTube videos. Starting from the intuition that the characteristics of the two media types are different yet complementary, we develop a fast and easily-parallelizable approach for creating not only high-quality video summaries but also novel structural summaries of online images as storyline graphs. The storyline graphs can illustrate various events or activities associated with the topic in a form of a branching network. The video summarization is achieved by diversity ranking on the similarity graphs between images and video frames. The reconstruction of storyline graphs is formulated as the inference of sparse time-varying directed graphs from a set of photo streams with assistance of videos. For evaluation, we collect the datasets of 20 outdoor activities, consisting of 2.7M Flickr images and 16K YouTube videos. Due to the large-scale nature of our problem, we evaluate our algorithm via crowdsourcing using Amazon Mechanical Turk. In our experiments, we demonstrate that the proposed joint summarization approach outperforms other baselines and our own methods using videos or images only.
Semi-supervised Relational Topic Model for Weakly Annotated Image Recognition in Social Media
Authors: Zhenxing Niu, Gang Hua, Xinbo Gao, Qi Tian
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

In this paper, we address the problem of recognizing images with weakly annotated text tags. Most previous work either cannot be applied to the scenarios where the tags are loosely related to the images; or simply take a pre-fusion at the feature level or a post-fusion at the decision level to combine the visual and textual content. Instead, we first encode the text tags as the relations among the images, and then propose a semi-supervised relational topic model (ss-RTM) to explicitly model the image content and their relations. In such way, we can efficiently leverage the loosely related tags, and build an intermediate level representation for a collection of weakly annotated images. The intermediate level representation can be regarded as a mid-level fusion of the visual and textual content, which is able to explicitly model their intrinsic relationships. Moreover, image category labels are also modeled in the ss-RTM, and recognition can be conducted without training an additional discriminative classifier. Our extensive experiments on social multimedia datasets (images+tags) demonstrated the advantages of the proposed model.
Beyond Human Opinion Scores: Blind Image Quality Assessment based on Synthetic Scores
Authors: Peng Ye, Jayant Kumar, David Doermann
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Blind Image Quality Assessment, Image Quality Assessment

State-of-the-art general purpose Blind Image Quality Assessment (BIQA) models rely on examples of distorted images and corresponding human opinion scores to learn a regression function that maps image features to a quality score. These types of models are considered "opinion-aware" (OA) BIQA models. A large set of human scored training examples is usually required to train a reliable OA-BIQA model. However, obtaining human opinion scores through subjective testing is often expensive and time-consuming. It is therefore desirable to develop "opinion-free" (OF) BIQA models that do not require human opinion scores for training.  This paper proposes BLISS (Blind Learning of Image Quality using Synthetic Scores). BLISS is a simple, yet effective method for extending OA-BIQA models to OF-BIQA models. Instead of training on human opinion scores, we propose to train BIQA models on synthetic scores derived from Full-Reference (FR) IQA measures. State-of-the-art FR measures yield high correlation with human opinion scores and can serve as approximations to human opinion scores. Unsupervised rank aggregation is applied to combine different FR measures to generate a synthetic score, which serves as a better "gold standard". Extensive experiments on standard IQA datasets show that BLISS significantly outperforms previous OF-BIQA methods and is comparable to state-of-the-art OA-BIQA methods.
Active Sampling for Subjective Image Quality Assessment
Authors: Peng Ye, David Doermann
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Quality Assessment

Subjective Image Quality Assessment (IQA) is the most reliable way to evaluate the visual quality of digital images perceived by the end user. It is often used to construct image quality datasets and provide the groundtruth for building and evaluating objective quality measures. Subjective tests based on the Mean Opinion Score (MOS) have been widely used in previous studies, but have many known problems such as an ambiguous scale definition and dissimilar interpretations of the scale among subjects. To overcome these limitations, Paired Comparison (PC) tests have been proposed as an alternative and are expected to yield more reliable results. However, PC tests can be expensive and time consuming, since for n images they require n choose 2 comparisons. We present a hybrid subjective test which combines MOS and PC tests via a unified probabilistic model and an active sampling method. The proposed method actively constructs a set of queries consisting of MOS and PC tests based on the expected information gain provided by each test and can effectively reduce the number of tests required for achieving a target accuracy. Our method can be used in conventional laboratory studies as well as crowdsourcing experiments. Experimental results show the proposed method outperforms state-of-the-art subjective IQA tests in a crowdsourced setting.
A Study on Cross-Population Age Estimation
Authors: Guodong Guo, Chao Zhang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Age Estimation, Human Aging, MORPH

We study the problem of cross-population age estimation. Human aging is determined by the genes and influenced by many factors. Different populations, e.g., males and females, Caucasian and Asian, may age differently. Previous research has discovered the aging difference among different populations, and reported large errors in age estimation when crossing gender and/or ethnicity. In this paper we propose novel methods for cross-population age estimation with a good performance. The proposed methods are based on projecting the different aging patterns into a common space where the aging patterns can be correlated even though they come from different populations. The projections are also discriminative between age classes due to the integration of the classical discriminant analysis technique. Further, we study the amount of data needed in the target population to learn a cross-population age estimator. Finally, we study the feasibility of multi-source cross-population age estimation. Experiments are conducted on a large database of more than 21,000 face images selected from the MORPH. Our studies are valuable to significantly reduce the burden of training data collection for age estimation on a new population, utilizing existing aging patterns even from different populations.