OpenRaiser/Intern-Atlas huggingface dataset:
(BP)2: Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies
Authors: Ifeoma Nwogu, Jason J. Corso
Venue: CVPR
Year: 2008

Belief propagation (BP) can be very useful and efficient for performing approximate inference on graphs. But when the graph is very highly connected with strong conflicting interactions, BP tends to fail to converge. Generalized Belief Propagation (GBP) provides more accurate solutions on such graphs, by approximating Kikuchi free energies, but the clusters required for the Kikuchi approximations are hard to generate. We propose a new algorithmic way of generating such clusters from a graph without exponentially increasing the size of the graph during triangulation. In order to perform the statistical region labeling, we introduce the use of superpixels for the nodes of the graph, as it is a more natural representation of an image than the pixel grid. This results in a smaller but much more highly interconnected graph where BP consistently fails. We demonstrate how our version of the GBP algorithm outperforms BP on synthetic and natural images and in both cases, GBP converges after only a few iterations.
A polynomial-time bound for matching and registration with outliers
Authors: Carl Olsson, Olof Enqvist, Fredrik Kahl
Venue: CVPR
Year: 2008

We present a framework for computing optimal transformations, aligning one point set to another, in the presence of outliers. Example applications include shape matching and registration (using, for example, similarity, affine or projective transformations) as well as multiview reconstruction problems (triangulation, camera pose etc.). While standard methods like RANSAC essentially use heuristics to cope with outliers, we seek to find the largest possible subset of consistent correspondences and the globally optimal transformation aligning the point sets. Based on theory from computational geometry, we show that this is indeed possible to accomplish in polynomial-time. We develop several algorithms which make efficient use of convex programming. The scheme has been tested and evaluated on both synthetic and real data for several applications.
Real-time 3D segmentation of the left ventricle using deformable subdivision surfaces
Authors: Fredrik Orderud, Stein I. Rabben
Venue: CVPR
Year: 2008

In this paper, we extend a computationally efficient framework for real-time 3D tracking and segmentation to support deformable subdivision surfaces. Segmentation is performed in a sequential state-estimation fashion, using an extended Kalman filter to estimate shape and pose parameters for the subdivision surface. As an example, we have integrated Doo-Sabin subdivision surfaces into the framework. Furthermore, we provide a method for evaluating basis functions for Doo-Sabin surfaces at arbitrary parameter values. These basis functions are precomputed during initialization, and later used during segmentation to quickly evaluate surface points used for edge detection. Fully automatic tracking and segmentation of the left ventricle is demonstrated in a dataset of 21 3D echocardiography recordings. Successful segmentation was achieved in all cases, with limits of agreement (mean plusmn1.96SD) for point to surface distance of 2.2 plusmn 0.8 mm compared to manually verified segmentations. Real-time segmentation at a rate of 25 frames per second consumed a CPU load of 8%.
Loose shape model for discriminative learning of object categories
Authors: Margarita Osadchy, Elran Morash
Venue: CVPR
Year: 2008

We consider the problem of visual categorization with minimal supervision during training. We propose a partbased model that loosely captures structural information. We represent images as a collection of parts characterized by an appearance codeword from a visual vocabulary and by a neighborhood context, organized in an ordered set of bag-of-features representations. These bags are computed in a local overlapping areas around the part. A semantic distance between images is obtained by matching parts associated with the same codeword using their context distributions. The classification is done using SVM with the kernel obtained from the proposed distance. The experiments show that our method outperforms all the classification methods from the PASCAL challenge on half of the VOC2006 categories and has the best average EER. It also outperforms the constellation model learned via boosting, as proposed by Bar-Hillel et al. on their data set, which contains more rigid objects.
Sequential sparsification for change detection
Authors: Necmiye Ozay, Mario Sznaier, Octavia I. Camps
Venue: CVPR
Year: 2008

This paper presents a general method for segmenting a vector valued sequence into an unknown number of subsequences where all data points from a subsequence can be represented with the same affine parametric model. The idea is to cluster the data into the minimum number of such subsequences which, as we show, can be cast as a sparse signal recovery problem by exploiting the temporal correlation between consecutive data points. We try to maximize the sparsity (i.e. the number of zero elements) of the first order differences of the sequence of parameter vectors. Each non-zero element in the first order difference sequence corresponds to a change. A weighted l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm based convex approximation is adopted to solve the change detection problem. We apply the proposed method to video segmentation and temporal segmentation of dynamic textures.
Macro-cuboïd based probabilistic matching for lip-reading digits
Authors: Samuel Pachoud, Shaogang Gong, Andrea Cavallaro
Venue: CVPR
Year: 2008

In this paper, we present a spatio-temporal feature representation and a probabilistic matching function to recognise lip movements from pronounced digits. Our model (1) automatically selects spatio-temporal features extracted from 10 digit model templates and (2) matches them with probe video sequences. Spatio-temporal features embed lip movements from pronouncing digits and contain more discriminative information than spatial features alone. A model template for each digit is represented by a set of spatio-temporal features at multiple scales. A probabilistic sequence matching function automatically segments a probe video sequence and matches the most likely sequence of digits recognised in the probe sequence. We demonstrate the proposed approach using the CUAVE database and compare our representational scheme with three alternative methods, based on optical flow, intensity gradient and block matching, respectively. The evaluation shows that the proposed approach outperforms the others in recognition accuracy and is robust in coping with variations in probe sequences.
Hallucinating 3D facial shapes
Authors: Gang Pan 0001, Song Han, Zhaohui Wu 0001
Venue: CVPR
Year: 2008

This paper focuses on hallucinating a facial shape from a low-resolution 3D facial shape. Firstly, we give a constrained conformal embedding of 3D shape in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , which establishes an isomorphic mapping between curved facial surface and 2D planar domain. With such conformal embedding, two planar representations of 3D shapes are proposed: <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gaussian</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">curvature</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image</b> (GCI) for a facial surface, and <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">surface</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">displacement</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image</b> (SDI) for a pair of facial surfaces. The conformal planar representation reduces the data complexity from 3D irregular curved surface to 2D regular grid while preserving the necessary information for hallucination. Then, hallucinating a low resolution facial shape is formalized as inference of SDI from GCIs by modeling the relationship between GCI and SDI by RBF regression. The experiments on USF HumanID 3D face database demonstrate the effectiveness of the approach. Our method can be easily extended to hallucinate those category-specific 3D surfaces sharing with similar geometric structures.
Adaptive and constrained algorithms for inverse compositional Active Appearance Model fitting
Authors: George Papandreou, Petros Maragos
Venue: CVPR
Year: 2008

Parametric models of shape and texture such as active appearance models (AAMs) are diverse tools for deformable object appearance modeling and have found important applications in both image synthesis and analysis problems. Among the numerous algorithms that have been proposed for AAM fitting, those based on the inverse-compositional image alignment technique have recently received considerable attention due to their potential for high efficiency. However, existing fitting algorithms perform poorly when used in conjunction with models exhibiting significant appearance variation, such as AAMs trained on multiple-subject human face images. We introduce two enhancements to inverse-compositional AAM matching algorithms in order to overcome this limitation. First, we propose fitting algorithm adaptation, by means of (a) fitting matrix adjustment and (b) AAM mean template update. Second, we show how prior information can be incorporated and constrain the AAM fitting process. The inverse-compositional nature of the algorithm allows efficient implementation of these enhancements. Both techniques substantially improve AAM fitting performance, as demonstrated with experiments on publicly available multi-face datasets.
From appearance to context-based recognition: Dense labeling in small images
Authors: Devi Parikh, C. Lawrence Zitnick, Tsuhan Chen
Venue: CVPR
Year: 2008

Traditionally, object recognition is performed based solely on the appearance of the object. However, relevant information also exists in the scene surrounding the object. As supported by our human studies, this contextual information is necessary for accurate recognition in low resolution images. This scenario with impoverished appearance information, as opposed to using images of higher resolution, provides an appropriate venue for studying the role of context in recognition. In this paper, we explore the role of context for dense scene labeling in small images. Given a segmentation of an image, our algorithm assigns each segment to an object category based on the segment’s appearance and contextual information. We explicitly model context between object categories through the use of relative location and relative scale, in addition to co-occurrence. We perform recognition tests on low and high resolution images, which vary significantly in the amount of appearance information present, using just the object appearance information, the combination of appearance and context, as well as just context without object appearance information (blind recognition). We also perform these tests in human studies and analyze our findings to reveal interesting patterns. With the use of our context model, our algorithm achieves state-of-the-art performance on MSRC and Corel. datasets.
Detection with multi-exit asymmetric boosting
Authors: Minh-Tri Pham, V-D. D. Hoang, Tat-Jen Cham
Venue: CVPR
Year: 2008

We introduce a generalized representation for a boosted classifier with multiple exit nodes, and propose a method to training which combines the idea of propagating scores across boosted classifiers [14, 17] and the use of asymmetric goals [13]. A means for determining the ideal constant asymmetric goal is provided, which is theoretically justified under a conservative bound on the ROC operating point target and empirically near-optimal under the exact bound. Moreover, our method automatically minimizes the number of weak classifiers, avoiding the need to retrain a boosted classifier multiple times for empirical best performance as in conventional methods. Experimental results shows significant reduction in training time and number of weak classifiers, as well as better accuracy, compared to conventional cascades and multi-exit boosted classifiers.
Robust learning of discriminative projection for multicategory classification on the Stiefel manifold
Authors: Duc-Son Pham 0001, Svetha Venkatesh
Venue: CVPR
Year: 2008

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.
Lost in quantization: Improving particular object retrieval in large scale image databases
Authors: James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, Andrew Zisserman
Venue: CVPR
Year: 2008

The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images are characterized using high-dimensional descriptors which are then mapped to ldquovisual wordsrdquo selected from a discrete vocabulary.This paper explores techniques to map each visual region to a weighted set of words, allowing the inclusion of features which were lost in the quantization stage of previous systems. The set of visual words is obtained by selecting words based on proximity in descriptor space. We describe how this representation may be incorporated into a standard tf-idf architecture, and how spatial verification is modified in the case of this soft-assignment. We evaluate our method on the standard Oxford Buildings dataset, and introduce a new dataset for evaluation. Our results exceed the current state of the art retrieval performance on these datasets, particularly on queries with poor initial recall where techniques like query expansion suffer. Overall we show that soft-assignment is always beneficial for retrieval with large vocabularies, at a cost of increased storage requirements for the index.
Light-invariant fitting of active appearance models
Authors: Daniel Pizarro 0001, Julien Peyras, Adrien Bartoli
Venue: CVPR
Year: 2008

This paper deals with shading and AAMs. Shading is created by lighting change. It can be of two types: self- shading and external shading. The effect of self-shading can be explicitly learned and handled by AAMs. This is not however possible for external shading, which is usually dealt with by robustifying the cost function. We take a different approach: we measure the fitting cost in a so-called Light-Invariant space. This approach naturally handles self-shading and external shading. The framework is based on mild assumptions on the scene reflectance and the cameras. Some photometric camera response parameters are required. We propose to estimate these while fitting an existing color AAM in a photometric 'self-calibration' manner. We report successful results with a face AAM with test images taken indoor under simple lighting change.
Using circular statistics for trajectory shape analysis
Authors: Andrea Prati 0001, Simone Calderara, Rita Cucchiara
Venue: CVPR
Year: 2008

The analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a mixture of Von Mises (MoVM) distribution. A complete EM (expectation maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. The similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data.
Two-Dimensional Active Learning for image classification
Authors: Guo-Jun Qi, Xian-Sheng Hua 0001, Yong Rui, Jinhui Tang 0001, Hong-Jiang Zhang
Venue: CVPR
Year: 2008

In this paper, we propose a two-dimensional active learning scheme and show its application in image classification. Traditional active learning methods select samples only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label classification. In multi-label classification, we argue that, for each selected sample, only a part of more effective labels are necessary to be annotated while others can be inferred by exploring the correlations among the labels. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multi-label Bayesian classification error bound. This new active learning strategy not only considers the sample dimension but also the label dimension, and we call it Two-Dimensional Active Learning (2DAL). We also show that the traditional active learning formulation is a special case of 2DAL when there is only one label. Extensive experiments conducted on two real-world applications show that the 2DAL significantly outperforms the best existing approaches which did not take label correlation into account.
A joint appearance-spatial distance for kernel-based image categorization
Authors: Guo-Jun Qi, Xian-Sheng Hua 0001, Yong Rui, Jinhui Tang 0001, Zheng-Jun Zha, Hong-Jiang Zhang
Venue: CVPR
Year: 2008

The goal of image categorization is to classify a collection of unlabeled images into a set of predefined classes to support semantic-level image retrieval. The distance measures used in most existing approaches either ignored the spatial structures or used them in a separate step. As a result, these distance measures achieved only limited success. To address these difficulties, in this paper, we propose a new distance measure that integrates joint appearance-spatial image features. Such a distance measure is computed as an upper bound of an information-theoretic discrimination, and can be computed efficiently in a recursive formulation that scales well to image size. In addition, the upper bound approximation can be further tightened via adaption learning from a universal reference model. Extensive experiments on two widely-used data sets show that the proposed approach significantly outperforms the state-of-the-art approaches.
Re-thinking non-rigid structure from motion
Authors: Vincent C. Rabaud, Serge J. Belongie
Venue: CVPR
Year: 2008

We present a novel approach to non-rigid structure from motion (NRSFM) from an orthographic video sequence, based on a new interpretation of the problem. Existing approaches assume the object shape space is well-modeled by a linear subspace. Our approach only assumes that small neighborhoods of shapes are well-modeled with a linear subspace. This constrains the shapes to belong to a manifold of dimensionality equal to the number of degrees of freedom of the object. After showing that the problem is still overconstrained, we present a solution composed of a novel initialization algorithm, followed by a robust extension of the Locally Smooth Manifold Learning algorithm tailored to the NRSFM problem. We finally present some test cases where the linear basis method fails (and is actually not meant to work) while the proposed approach is successful. 1.
Increasing the density of Active Appearance Models
Authors: Krishnan Ramnath, Simon Baker, Iain A. Matthews, Deva Ramanan
Venue: CVPR
Year: 2008

Active appearance models (AAMs) typically only use 50-100 mesh vertices because they are usually constructed from a set of training images with the vertices hand-labeled on them. In this paper, we propose an algorithm to increase the density of an AAM. Our algorithm operates by iteratively building the AAM, refitting the AAM to the training data, and refining the AAM.We compare our algorithm with the state of the art in optical flow algorithms and find it to be significantly more accurate. We also show that dense AAMs can be fit more robustly than sparse ones. Finally, we show how our algorithm can be used to construct AAMs automatically, starting with a single affine model that is subsequently refined to model non-planarity and non-rigidity.
Recognising faces in unseen modes: A tensor based approach
Authors: Santu Rana, Wanquan Liu, Mihai M. Lazarescu, Svetha Venkatesh
Venue: CVPR
Year: 2008

This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost.
Tracking rotating fluids in realtime using snapshots
Authors: Sai Ravela, John Marshall, Christopher Hill, Andrew Wong, Scott Stransky
Venue: CVPR
Year: 2008

We present a model-based system for tracking rotating fluids, and apply it to a laboratory study of atmospheric circulation. Tracking is accomplished by filtering uncertain and high-dimensional states of a nonlinear general circulation model with optical measurements of the physical fluidpsilas velocity. Realtime performance is achieved by using a nonuniform discretization of the modelpsilas spatial resolution, and by using time-snapshots of model-state to construct spatially-localized reduced-rank square-root representations of forecast uncertainty. Realtime performance, economical and repeatable experimentation, and a direct connection to planetary flows implies that the proposed physical-numerical coupling can be useful for addressing many perceptual geophysical fluid dynamics problems. To the best of our knowledge, such a system has not hitherto been reported.
High resolution matting via interactive trimap segmentation
Authors: Christoph Rhemann, Carsten Rother, Alex Rav-Acha, Toby Sharp
Venue: CVPR
Year: 2008

We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques.