OpenRaiser/Intern-Atlas huggingface dataset:
Slow feature analysis and decorrelation filtering for separating correlated sources
Authors: Ha Quang Minh, Laurenz Wiskott
Venue: ICCV
Year: 2011

We generalize the method of Slow Feature Analysis for vector-valued functions of multivariables and apply it to the problem of blind source separation, in particular image separation. For the linear case, exact mathematical analysis is given, which shows in particular that the sources are perfectly separated by SFA if and only if they and their first order derivatives are uncorrelated. When the sources are correlated, we apply the following technique called decorrelation filtering: use a linear filter to decorrelate the sources and their derivatives, then apply the separating matrix obtained on the filtered sources to the original sources. We show that if the filtered sources are perfectly separated by this matrix, then so are the original sources. We show how to numerically obtain such a decorrelation filter by solving a nonlinear optimization problem. This technique can also be applied to other linear separation methods, whose output signals are decorrelated, such as ICA.
Point-based calibration using a parametric representation of the general imaging model
Authors: Pedro Miraldo, Helder Araújo, Joao Queiro
Venue: ICCV
Year: 2011

Generic imaging models can be used to represent any camera. These models are specially suited for non-central cameras for which closed-form models do not exist. Current models are discrete and define a mapping between each pixel in the image and a straight line in 3D space. Due to difficulties in the calibration procedure and model complexity these methods have not been used in practice. The focus of our work was to relax these drawbacks. In this paper we modify the general imaging model using radial basis functions to interpolate image coordinates and 3D lines allowing both an increase in resolution (due to their continuous nature) and a more compact representation. Using this new variation of the general imaging model we also develop a new linear calibration procedure. In this process it is only required to match one 3D point to each image pixel. Also it is not required the calibration of every image pixel. As a result the complexity of the procedure is significantly decreased.
Optimal estimation of vanishing points in a Manhattan world
Authors: Faraz M. Mirzaei, Stergios I. Roumeliotis
Venue: ICCV
Year: 2011

In this paper, we present an analytical method for computing the globally optimal estimates of orthogonal vanishing points in a “Manhattan world” with a calibrated camera. We formulate this as constrained least-squares problem whose optimality conditions form a multivariate polynomial system. We solve this system analytically to compute all the critical points of the least-squares cost function, and hence the global minimum, i.e., the globally optimal estimate for the orthogonal vanishing points. The same optimal estimator is used in conjunction with RANSAC to generate orthogonal-vanishing-point hypotheses (from triplets of lines) and thus classify lines into parallel and mutually orthogonal groups. The proposed method is validated experimentally on the York Urban Database.
Compact correlation coding for visual object categorization
Authors: Nobuyuki Morioka, Shin&apos, ichi Satoh 0001
Venue: ICCV
Year: 2011

Spatial relationships between local features are thought to play a vital role in representing object categories. However, learning a compact set of higher-order spatial features based on visual words, e.g., doublets and triplets, remains a challenging problem as possible combinations of visual words grow exponentially. While the local pairwise codebook achieves a compact codebook of pairs of spatially close local features without feature selection, its formulation is not scale invariant and is only suitable for densely sampled local features. In contrast, the proximity distribution kernel is a scale-invariant and robust representation capturing rich spatial proximity information between local features, but its representation grows quadratically in the number of visual words. Inspired by the two abovementioned techniques, this paper presents the compact correlation coding that combines the strengths of the two. Our method achieves a compact representation that is scaleinvariant and robust against object deformation. In addition, we adopt sparse coding instead of k-means clustering during the codebook construction to increase the discriminative power of our method. We systematically evaluate our method against both the local pairwise codebook and proximity distribution kernel on several challenging object categorization datasets to show performance improvements.
Fourier Active Appearance Models
Authors: Rajitha Navarathna, Sridha Sridharan, Simon Lucey
Venue: ICCV
Year: 2011

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.
Automatic construction of an action video shot database using web videos
Authors: Do Hang Nga, Keiji Yanai
Venue: ICCV
Year: 2011

There are a huge number of videos with text tags on the Web nowadays. In this paper, we propose a method of automatically extracting from Web videos video shots corresponding to specific actions with just only providing action keywords such as “walking” and “eating”. The proposed method consists of three steps: (1) tag-based video selection, (2) segmenting videos into shots and extracting features from the shots, and (3) visual-feature-based video shot selection with tag-based scores taken into account. Firstly, we gather video IDs and tag lists for 1000 Web videos corresponding to given keywords via Web API, and we calculate tag relevance scores for each video using a tag-co-occurrence dictionary which is constructed in advance. Secondly, we fetch the top 200 videos from the Web in the descending order of the tag relevance scores, and segment each downloaded video into several shots. From each shot we extract spatio-temporal features, global motion features and appearance features, and convert them into the bag-of-features representation. Finally, we apply the VisualRank method to select the video shots which describe the actions corresponding to the given keywords best after calculating a similarity matrix between video shots. In the experiments, we achieved the 49.5% precision at 100 shots over six kinds of human actions by just providing keywords without any supervision. In addition, we made large-scale experiments on 100 kinds of action keywords.
Unsupervised and semi-supervised learning via ℓ1-norm graph
Authors: Feiping Nie 0001, Hua Wang 0007, Heng Huang 0001, Chris H. Q. Ding
Venue: ICCV
Year: 2011

In this paper, we propose a novel ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm graph model to perform unsupervised and semi-supervised learning methods. Instead of minimizing the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of spectral embedding as traditional graph based learning methods, our new graph learning model minimizes the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm of spectral embedding with well motivation. The sparsity produced by the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization results in the solutions with much clearer cluster structures, which are suitable for both image clustering and classification tasks. We introduce a new efficient iterative algorithm to solve the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm of spectral embedding minimization problem, and prove the convergence of the algorithm. More specifically, our algorithm adaptively re-weight the original weights of graph to discover clearer cluster structure. Experimental results on both toy data and real image data sets show the effectiveness and advantages of our proposed method.
Decision tree fields
Authors: Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, Pushmeet Kohli
Venue: ICCV
Year: 2011

This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers, however, the pairwise potentials are assumed to (1) have a simple parametric form with a pre-specified and fixed dependence on the image data, and (2) to be defined on the basis of a small and fixed neighborhood. In contrast, in DTF, local interactions between multiple variables are determined by means of decision trees evaluated on the image data, allowing the interactions to be adapted to the image content. This results in powerful graphical models which are able to represent complex label structure. Our key technical contribution is to show that the DTF model can be trained efficiently and jointly using a convex approximate likelihood function, enabling us to learn over a million free model parameters. We show experimentally that for applications which have a rich and complex label structure, our model achieves excellent results.
Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions
Authors: Peter Ochs, Thomas Brox
Venue: ICCV
Year: 2011

Point trajectories have emerged as a powerful means to obtain high quality and fully unsupervised segmentation of objects in video shots. They can exploit the long term motion difference between objects, but they tend to be sparse due to computational reasons and the difficulty in estimating motion in homogeneous areas. In this paper we introduce a variational method to obtain dense segmentations from such sparse trajectory clusters. Information is propagated with a hierarchical, nonlinear diffusion process that runs in the continuous domain but takes superpixels into account. We show that this process raises the density from 3% to 100% and even increases the average precision of labels.
Efficient algorithm for low-rank matrix factorization with missing components and performance comparison of latest algorithms
Authors: Takayuki Okatani, Takahiro Yoshida, Koichiro Deguchi
Venue: ICCV
Year: 2011

This paper examines numerical algorithms for factorization of a low-rank matrix with missing components. We first propose a new method that incorporates a damping factor into the Wiberg method to solve the problem. The new method is characterized by the way it constrains the ambiguity of the matrix factorization, which helps improve both the global convergence ability and the local convergence speed. We then present experimental comparisons with the latest methods used to solve the problem. No comprehensive comparison of the methods that have been proposed recently has yet been reported in literature. In our experiments, we prioritize the assessment of the global convergence performance of each method, that is, how often and how fast the method can reach the global optimum starting from random initial values. Our conclusion is that top performance is achieved by a group of methods based on Newton-family minimization with damping factor that reduce the problem by eliminating either of the two factored matrices. Our method, which belongs to this group, consistently shows a 100% global convergence rate for different types of affine structure from motion data with a very high population of missing components.
Scene recognition and weakly supervised object localization with deformable part-based models
Authors: Megha Pandey, Svetlana Lazebnik
Venue: ICCV
Year: 2011

Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based models (DPM's) with latent SVM training [6]. These models have been introduced for fully supervised training of object detectors, but we demonstrate that they are also capable of more open-ended learning of latent structure for such tasks as scene recognition and weakly supervised object localization. For scene recognition, DPM's can capture recurring visual elements and salient objects; in combination with standard global image features, they obtain state-of-the-art results on the MIT 67-category indoor scene dataset. For weakly supervised object localization, optimization over latent DPM parameters can discover the spatial extent of objects in cluttered training images without ground-truth bounding boxes. The resulting method outperforms a recent state-of-the-art weakly supervised object localization approach on the PASCAL-07 dataset.
Recognizing jumbled images: The role of local and global information in image classification
Authors: Devi Parikh
Venue: ICCV
Year: 2011

The performance of current state-of-the-art computer vision algorithms at image classification falls significantly short as compared to human abilities. To reduce this gap, it is important for the community to know what problems to solve, and not just how to solve them. Towards this goal, via the use of jumbled images, we strip apart two widely investigated aspects: local and global information in images, and identify the performance bottleneck. Interestingly, humans have been shown to reliably recognize jumbled images. The goal of our paper is to determine a functional model that mimics how humans recognize jumbled images i.e. exploit local information alone, and further evaluate if existing implementations of this computational model suffice to match human performance. Surprisingly, in our series of human studies and machine experiments, we find that a simple bag-of-words based majority-vote-like strategy is an accurate functional model of how humans recognize jumbled images. Moreover, a straightforward machine implementation of this model achieves accuracies similar to human subjects at classifying jumbled images. This indicates that perhaps existing machine vision techniques already leverage local information from images effectively, and future research efforts should be focused on more advanced modeling of global information.
Relative attributes
Authors: Devi Parikh, Kristen Grauman
Venue: ICCV
Year: 2011

Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is `smiling' or not, a scene is `dry' or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, `bears are furrier than giraffes'). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.
Simplification of 3D morphable models
Authors: Ankur Patel, William A. P. Smith
Venue: ICCV
Year: 2011

In this paper we show how to simplify a 3D morphable model. Our method only requires knowledge of the original highest resolution statistical model and leads to low resolution models in which the model statistics are a subset of the original high resolution model. We employ an iterative edge collapse strategy, where the deleted edge is chosen as a function of the model statistics. We show that the expected value of the Quadric Error Metric can be computed in closed form for a PCA deformable model. Model parameters obtained using the model at any resolution (lower) can be used to reconstruct a high resolution surface, providing a route to super-resolution. We provide experimental results for a statistical face model, showing how the simplified models improve the efficiency of model fitting. We are able to decrease the model resolution and fitting time by factors of approximately 10 and 4 respectively whilst inducing an error which is only slightly larger than the fitting error of the original model.
From contours to 3D object detection and pose estimation
Authors: Nadia Payet, Sinisa Todorovic
Venue: ICCV
Year: 2011

This paper addresses view-invariant object detection and pose estimation from a single image. While recent work focuses on object-centered representations of point-based object features, we revisit the viewer-centered framework, and use image contours as basic features. Given training examples of arbitrary views of an object, we learn a sparse object model in terms of a few view-dependent shape templates. The shape templates are jointly used for detecting object occurrences and estimating their 3D poses in a new image. Instrumental to this is our new mid-level feature, called bag of boundaries (BOB), aimed at lifting from individual edges toward their more informative summaries for identifying object boundaries amidst the background clutter. In inference, BOBs are placed on deformable grids both in the image and the shape templates, and then matched. This is formulated as a convex optimization problem that accommodates invariance to non-rigid, locally affine shape deformations. Evaluation on benchmark datasets demonstrates our competitive results relative to the state of the art.
Parsing video events with goal inference and intent prediction
Authors: Mingtao Pei, Yunde Jia, Song-Chun Zhu
Venue: ICCV
Year: 2011

In this paper, we present an event parsing algorithm based on Stochastic Context Sensitive Grammar (SCSG) for understanding events, inferring the goal of agents, and predicting their plausible intended actions. The SCSG represents the hierarchical compositions of events and the temporal relations between the sub-events. The alphabets of the SCSG are atomic actions which are defined by the poses of agents and their interactions with objects in the scene. The temporal relations are used to distinguish events with similar structures, interpolate missing portions of events, and are learned from the training data. In comparison with existing methods, our paper makes the following contributions. i) We define atomic actions by a set of relations based on the fluents of agents and their interactions with objects in the scene. ii) Our algorithm handles events insertion and multi-agent events, keeps all possible interpretations of the video to preserve the ambiguities, and achieves the globally optimal parsing solution in a Bayesian framework; iii) The algorithm infers the goal of the agents and predicts their intents by a top-down process; iv) The algorithm improves the detection of atomic actions by event contexts. We show satisfactory results of event recognition and atomic action detection on the data set we captured which contains 12 event categories in both indoor and outdoor videos.
On the repeatability of the local reference frame for partial shape matching
Authors: Alioscia Petrelli, Luigi Di Stefano
Venue: ICCV
Year: 2011

We investigate on local reference frames (LRF) deployed with 3D descriptors to achieve invariance to objects' pose. We address the task of matching together partial views of surfaces and propose an experimental study on a large corpus of real data which allows for clearly ranking existing LRF proposals based on their repeatability. Then, drawing inspiration from analysis of the experimental findings, we formulate a new proposal which, in particular, peculiarly includes a procedure aimed at estimating a repeatable LRF also at border features, which is very important when matching partial views of surfaces. Experiments show that the new proposal neatly outperforms existing methods in terms of repeatability, is computationally very efficient and provide relevant benefits in practical applications
A new distance for scale-invariant 3D shape recognition and registration
Authors: Minh-Tri Pham, Oliver J. Woodford, Frank Perbet, Atsuto Maki, Björn Stenger, Roberto Cipolla
Venue: ICCV
Year: 2011

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this space-the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.
Diagonal preconditioning for first order primal-dual algorithms in convex optimization
Authors: Thomas Pock, Antonin Chambolle
Venue: ICCV
Year: 2011

In this paper we study preconditioning techniques for the first-order primal-dual algorithm proposed in [5]. In particular, we propose simple and easy to compute diagonal preconditioners for which convergence of the algorithm is guaranteed without the need to compute any step size parameters. As a by-product, we show that for a certain instance of the preconditioning, the proposed algorithm is equivalent to the old and widely unknown alternating step method for monotropic programming [7]. We show numerical results on general linear programming problems and a few standard computer vision problems. In all examples, the preconditioned algorithm significantly outperforms the algorithm of [5].