OpenRaiser/Intern-Atlas huggingface dataset:
Reduced epipolar cost for accelerated incremental SfM
Authors: Antonio L. Rodríguez, Pedro E. López-de-Teruel, Alberto Ruiz
Venue: CVPR
Year: 2011

We propose a reduced algebraic cost based on pairwise epipolar constraints for the iterative refinement of a multiple view 3D reconstruction. The aim is to accelerate the intermediate steps required when incrementally building a reconstruction from scratch. Though the proposed error is algebraic, careful input data normalization makes it a good approximation to the true geometric epipolar distance. Its minimization is significantly faster and obtains a geometric reprojection error very close to the optimum value, requiring very few iterations of final standard BA refinement. Smart usage of a reduced measurement matrix for each pair of views allows elimination of the variables corresponding to the 3D points prior to nonlinear optimization, subsequently reducing computation, memory usage, and considerably accelerating convergence. This approach has been tested in a wide range of real and synthetic problems, consistently obtaining significant robustness and convergence improvements even when starting from rough initial solutions. Its efficiency and scalability make it thus an ideal choice for incremental SfM in real-time tracking applications or scene modelling from large image databases.
Evaluating knowledge transfer and zero-shot learning in a large-scale setting
Authors: Marcus Rohrbach, Michael Stark 0003, Bernt Schiele
Venue: CVPR
Year: 2011

While knowledge transfer (KT) between object classes has been accepted as a promising route towards scalable recognition, most experimental KT studies are surprisingly limited in the number of object classes considered. To support claims of KT w.r.t. scalability we thus advocate to evaluate KT in a large-scale setting. To this end, we provide an extensive evaluation of three popular approaches to KT on a recently proposed large-scale data set, the ImageNet Large Scale Visual Recognition Competition 2010 data set. In a first setting they are directly compared to one-vs-all classification often neglected in KT papers and in a second setting we evaluate their ability to enable zero-shot learning. While none of the KT methods can improve over one-vs-all classification they prove valuable for zero-shot learning, especially hierarchical and direct similarity based KT. We also propose and describe several extensions of the evaluated approaches that are necessary for this large-scale study.
Human brain labeling using image similarities
Authors: François Rousseau 0002, Piotr A. Habas, Colin Studholme
Venue: CVPR
Year: 2011

We propose in this work a patch-based segmentation method relying on a label propagation framework. Based on image intensity similarities between the input image and a learning dataset, an original strategy which does not require any non-rigid registration is presented. Following recent developments in non-local image denoising, the similarity between images is represented by a weighted graph computed from intensity-based distance between patches. Experiments on simulated and in-vivo MR images show that the proposed method is very successful in providing automated human brain labeling.
Energy based multiple model fitting for non-rigid structure from motion
Authors: Chris Russell 0001, João Fayad, Lourdes Agapito
Venue: CVPR
Year: 2011

In this paper we reformulate the 3D reconstruction of deformable surfaces from monocular video sequences as a labeling problem. We solve simultaneously for the assignment of feature points to multiple local deformation models and the fitting of models to points to minimize a geometric cost, subject to a spatial constraint that neighboring points should also belong to the same model. Piecewise reconstruction methods rely on features shared between models to enforce global consistency on the 3D surface. To account for this overlap between regions, we consider a super-set of the classic labeling problem in which a set of labels, instead of a single one, is assigned to each variable. We propose a mathematical formulation of this new model and show how it can be efficiently optimized with a variant of α-expansion. We demonstrate how this framework can be applied to Non-Rigid Structure from Motion and leads to simpler explanations of the same data. Compared to existing methods run on the same data, our approach has up to half the reconstruction error, and is more robust to over-fitting and outliers.
Learning to share visual appearance for multiclass object detection
Authors: Ruslan Salakhutdinov, Antonio Torralba 0001, Joshua B. Tenenbaum
Venue: CVPR
Year: 2011

We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.
High-dimensional signature compression for large-scale image classification
Authors: Jorge Sánchez 0002, Florent Perronnin
Venue: CVPR
Year: 2011

We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases - ImageNet and a dataset of lM Flickr images - showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.
Object association across PTZ cameras using logistic MIL
Authors: Karthik Sankaranarayanan, James W. Davis
Venue: CVPR
Year: 2011

We propose a novel approach to associate objects across multiple PTZ cameras that can be used to perform camera handoff in wide-area surveillance scenarios. While previous approaches relied on geometric, appearance, or correlation-based information for establishing correspondences between static cameras, they each have well-known limitations and are not extendable to wide-area settings with PTZ cameras. In our approach, the slave camera only passively follows the target (by loose registration with the master) and bootstraps itself from its own incoming imagery, thus effectively circumventing the problems faced by previous approaches and avoiding the need to perform any model transfer. Towards this goal, we also propose a novel Multiple Instance Learning (MIL) formulation for the problem based on the logistic softmax function of covariance-based region features within a MAP estimation framework. We demonstrate our approach with multiple PTZ camera sequences in typical outdoor surveillance settings and show a comparison with state-of-the-art approaches.
Parsing human motion with stretchable models
Authors: Benjamin Sapp, David J. Weiss, Ben Taskar
Venue: CVPR
Year: 2011

We address the problem of articulated human pose estimation in videos using an ensemble of tractable models with rich appearance, shape, contour and motion cues. In previous articulated pose estimation work on unconstrained videos, using temporal coupling of limb positions has made little to no difference in performance over parsing frames individually. One crucial reason for this is that joint parsing of multiple articulated parts over time involves intractable inference and learning problems, and previous work has resorted to approximate inference and simplified models. We overcome these computational and modeling limitations using an ensemble of tractable submodels which couple locations of body joints within and across frames using expressive cues. Each submodel is responsible for tracking a single joint through time (e.g., left elbow) and also models the spatial arrangement of all joints in a single frame. Because of the tree structure of each submodel, we can perform efficient exact inference and use rich temporal features that depend on image appearance, e.g., color tracking and optical flow contours. We propose and experimentally investigate a hierarchy of submodel combination methods, and we find that a highly efficient max-marginal combination method outperforms much slower (by orders of magnitude) approximate inference using dual decomposition. We apply our pose model on a new video dataset of highly varied and articulated poses from TV shows. We show significant quantitative and qualitative improvements over state-of-the-art single-frame pose estimation approaches.
Principal regression analysis
Authors: Jason M. Saragih
Venue: CVPR
Year: 2011

A new paradigm for multivariate regression is proposed; principal regression analysis (PRA). It entails learning a low dimensional subspace over sample-specific regressors. For a given input, the model predicts a subspace thought to contain the corresponding response. Using this subspace as a prior, the search space is considerably more constrained. An efficient local optimisation strategy is proposed for learning and a practical choice for its initialisation suggested. The utility of PRA is demonstrated on the task of non-rigid face and car alignment using challenging "in the wild" datasets, where substantial performance improvements are observed over alignment with a conventional prior.
A study of Nesterov's scheme for Lagrangian decomposition and MAP labeling
Authors: Bogdan Savchynskyy, Jörg H. Kappes, Stefan Schmidt 0001, Christoph Schnörr
Venue: CVPR
Year: 2011

We study the MAP-labeling problem for graphical models by optimizing a dual problem obtained by Lagrangian decomposition. In this paper, we focus specifically on Nes-terov's optimal first-order optimization scheme for non-smooth convex programs, that has been studied for a range of other problems in computer vision and machine learning in recent years. We show that in order to obtain an efficiently convergent iteration, this approach should be augmented with a dynamic estimation of a corresponding Lip-schitz constant, leading to a runtime complexity of O(1/ϵ) in terms of the desired precision ϵ. Additionally, we devise a stopping criterion based on a duality gap as a sound basis for competitive comparison and show how to compute it efficiently. We evaluate our results using the publicly available Middlebury database and a set of computer generated graphical models that highlight specific aspects, along with other state-of-the-art methods for MAP-inference.
Connecting non-quadratic variational models and MRFs
Authors: Kevin Schelten, Stefan Roth 0001
Venue: CVPR
Year: 2011

Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approaches are ubiquitous in low-level vision, including image restoration, segmentation, optical flow, and stereo. Even though both families of approaches are fairly similar on an intuitive level, they are frequently seen as being technically rather distinct since they operate on different domains. In this paper we explore their connections and develop a direct, rigorous link with a particular emphasis on first-order regularizers. By representing spatially-continuous functions as linear combinations of finite elements with local support and performing explicit integration of the variational objective, we derive MRF potentials that make the resulting MRF energy equivalent to the variational energy functional. In contrast to previous attempts, we provide an explicit connection for modern non-quadratic regularizers and also integrate the data term. The established connection opens certain classes of MRFs to spatially-continuous interpretations and variational formulations to a broad range of probabilistic learning and inference algorithms.
Distributed message passing for large scale graphical models
Authors: Alexander G. Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
Venue: CVPR
Year: 2011

In this paper we propose a distributed message-passing algorithm for inference in large scale graphical models. Our method can handle large problems efficiently by distributing and parallelizing the computation and memory requirements. The convergence and optimality guarantees of recently developed message-passing algorithms are preserved by introducing new types of consistency messages, sent between the distributed computers. We demonstrate the effectiveness of our approach in the task of stereo reconstruction from high-resolution imagery, and show that inference is possible with more than 200 labels in images larger than 10 MPixels.
Adaptive random forest - How many "experts" to ask before making a decision?
Authors: Alexander G. Schwing, Christopher Zach, Yefeng Zheng 0001, Marc Pollefeys
Venue: CVPR
Year: 2011

How many people should you ask if you are not sure about your way? We provide an answer to this question for Random Forest classification. The presented method is based on the statistical formulation of confidence intervals and conjugate priors for binomial as well as multinomial distributions. We derive appealing decision rules to speed up the classification process by leveraging the fact that many samples can be clearly mapped to classes. Results on test data are provided, and we highlight the applicability of our method to a wide range of problems. The approach introduces only one non-heuristic parameter, that allows to trade-off accuracy and speed without any re-training of the classifier. The proposed method automatically adapts to the difficulty of the test data and makes classification significantly faster without deteriorating the accuracy.
Majorization-minimization mixture model determination in image segmentation
Authors: Giorgos Sfikas, Christophoros Nikou, Nikolas P. Galatsanos, Christian Heinrich
Venue: CVPR
Year: 2011

A new Bayesian model for image segmentation based on a Gaussian mixture model is proposed. The model structure allows the automatic determination of the number of segments while ensuring spatial smoothness of the final output. This is achieved by defining two separate mixture weight sets: the first set of weights is spatially variant and incorporates an MRF edge-preserving smoothing prior; the second set of weights is governed by a Dirichlet prior in order to prune unnecessary mixture components. The model is trained using variational inference and the Majorization-Minimization (MM) algorithm, resulting in closed-form parameter updates. The algorithm was successfully evaluated in terms of various segmentation indices using the Berkeley image data base.
A scalable dual approach to semidefinite metric learning
Authors: Chunhua Shen, Junae Kim, Lei Wang 0001
Venue: CVPR
Year: 2011

Distance metric learning plays an important role in many vision problems. Previous work of quadratic Mahalanobis metric learning usually needs to solve a semidefinite programming (SDP) problem. A standard interior-point SDP solver has a complexity of O(D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6.5</sup> ) (with D the dimension of input data), and can only solve problems up to a few thousand variables. Since the number of variables is D(D + l)/2, this corresponds to a limit around D <; 100. This high complexity hampers the application of metric learning to high-dimensional problems. In this work, we propose a very efficient approach to this metric learning problem. We formulate a Lagrange dual approach which is much simpler to optimize, and we can solve much larger Mahalanobis metric learning problems. Roughly, the proposed approach has a time complexity of O(t · D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) with t ≈ 20 ~ 30 for most problems in our experiments. The proposed algorithm is scalable and easy to implement. Experiments on various datasets show its similar accuracy compared with state-of-the-art. We also demonstrate that this idea may also be able to be applied to other SDP problems such as maximum variance unfolding.
Intrinsic images decomposition using a local and global sparse representation of reflectance
Authors: Li Shen 0003, Chuohao Yeo
Venue: CVPR
Year: 2011

Intrinsic image decomposition is an important problem that targets the recovery of shading and reflectance components from a single image. While this is an ill-posed problem on its own, we propose a novel approach for intrinsic image decomposition using a reflectance sparsity prior that we have developed. Our method is based on a simple observation: neighboring pixels usually have the same reflectance if their chromaticities are the same or very similar. We formalize this sparsity constraint on local reflectance, and derive a sparse representation of reflectance components using data-driven edge-avoiding-wavelets. We show that the reflectance component of natural images is sparse in this representation. We also propose and formulate a novel global reflectance sparsity constraint. Using this sparsity prior and global constraints, we formulate a l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized least squares minimization problem for intrinsic image decomposition that can be solved efficiently. Our algorithm can successfully extract intrinsic images from a single image, without using other reflection or color models or any user interaction. The results on challenging scenes demonstrate the power of the proposed technique.
Is face recognition really a Compressive Sensing problem?
Authors: Qinfeng Shi, Anders P. Eriksson, Anton van den Hengel, Chunhua Shen
Venue: CVPR
Year: 2011

Compressive Sensing has become one of the standard methods of face recognition within the literature. We show, however, that the sparsity assumption which underpins much of this work is not supported by the data. This lack of sparsity in the data means that compressive sensing approach cannot be guaranteed to recover the exact signal, and therefore that sparse approximations may not deliver the robustness or performance desired. In this vein we show that a simple ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> approach to the face recognition problem is not only significantly more accurate than the state-of-the-art approach, it is also more robust, and much faster. These results are demonstrated on the publicly available YaleB and AR face datasets but have implications for the application of Compressive Sensing more broadly.
Real-time human pose recognition in parts from single depth images
Authors: Jamie Shotton, Andrew W. Fitzgibbon, Mat Cook, Toby Sharp, Mark Finocchio, Richard Moore 0003, Alex Kipman, Andrew Blake 0001
Venue: CVPR
Year: 2011

We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.
Image ranking and retrieval based on multi-attribute queries
Authors: Behjat Siddiquie, Rogério Schmidt Feris, Larry S. Davis
Venue: CVPR
Year: 2011

We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several state-of-the-art ranking and retrieval methods.
Efficient groupwise non-rigid registration of textured surfaces
Authors: Kirill A. Sidorov, Stephen Richmond, A. David Marshall
Venue: CVPR
Year: 2011

Advances in 3D imaging have recently made 3D surface scanners, capable of capturing textured surfaces at video rate, affordable and common in computer vision. This is a relatively new source of data, the potential of which has not yet been fully exploited as the problem of non-rigid registration of surfaces is difficult. While registration based on shape alone has been an active research area for some time, the problem of registering surfaces based on texture information has not been addressed in a principled way. We propose a novel, efficient and reliable, fully automatic method for performing groupwise non-rigid registration of textured surfaces, such as those obtained with 3D scanners. We demonstrate the robustness of our approach on 3D scans of human faces, including the notoriously difficult case of inter-subject registration. We show how our method can be used to build high-quality 3D models of appearance fully automatically.