OpenRaiser/Intern-Atlas huggingface dataset:
Global contrast based salient region detection
Authors: Ming-Ming Cheng, Guo-Xin Zhang, Niloy J. Mitra, Xiaolei Huang 0001, Shi-Min Hu 0001
Venue: CVPR
Year: 2011

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.
Dirichlet process mixture models on symmetric positive definite matrices for appearance clustering in video surveillance applications
Authors: Anoop Cherian, Vassilios Morellas, Nikolaos Papanikolopoulos, Saad Bedros
Venue: CVPR
Year: 2011

Covariance matrices of multivariate data capture feature correlations compactly, and being very robust to noise, they have been used extensively as feature descriptors in many areas in computer vision, like, people appearance tracking, DTI imaging, face recognition, etc. Since these matrices do not adhere to the Euclidean geometry, clustering algorithms using the traditional distance measures cannot be directly extended to them. Prior work in this area has been restricted to using K-means type clustering over the Rieman-nian space using the Riemannian metric. As the applications scale, it is not practical to assume the number of components in a clustering model, failing any soft-clustering algorithm. In this paper, a novel application of the Dirich-let Process Mixture Model framework is proposed towards unsupervised clustering of symmetric positive definite matrices. We approach the problem by extending the existing K-means type clustering algorithms based on the logdet divergence measure and derive the counterpart of it in a Bayesian framework, which leads to the Wishart-Inverse Wishart conjugate pair. Alternative possibilities based on the matrix Frobenius norm and log-Euclidean measures are also proposed. The models are extensively compared using two real-world datasets against the state-of-the-art algorithms and demonstrate superior performance.
Blur kernel estimation using the radon transform
Authors: Taeg Sang Cho, Sylvain Paris, Berthold K. P. Horn, William T. Freeman
Venue: CVPR
Year: 2011

Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel. Building upon this result, we describe two algorithms for estimating spatially invariant blur kernels. In the first method, we directly invert the transform, which is computationally efficient since it is not necessary to also estimate the latent sharp image. This approach is well suited for scenes with a diversity of edges, such as man-made environments. In the second method, we incorporate the Radon transform within the MAP estimation framework to jointly estimate the kernel and the image. While more expensive, this algorithm performs well on a broader variety of scenes, even when fewer edges can be observed. Our experiments show that our algorithms achieve comparable results to the state of the art in general and produce superior outputs on man-made scenes and photos degraded by a small kernel.
Learning context for collective activity recognition
Authors: Wongun Choi, Khuram Shahid, Silvio Savarese
Venue: CVPR
Year: 2011

In this paper we present a framework for the recognition of collective human activities. A collective activity is defined or reinforced by the existence of coherent behavior of individuals in time and space. We call such coherent behavior `Crowd Context'. Examples of collective activities are “queuing in a line” or “talking”. Following, we propose to recognize collective activities using the crowd context and introduce a new scheme for learning it automatically. Our scheme is constructed upon a Random Forest structure which randomly samples variable volume spatio-temporal regions to pick the most discriminating attributes for classification. Unlike previous approaches, our algorithm automatically finds the optimal configuration of spatio-temporal bins, over which to sample the evidence, by randomization. This enables a methodology for modeling crowd context. We employ a 3D Markov Random Field to regularize the classification and localize collective activities in the scene. We demonstrate the flexibility and scalability of the proposed framework in a number of experiments and show that our method outperforms state-of-the art action classification techniques.
Gated classifiers: Boosting under high intra-class variation
Authors: Oscar M. Danielsson, Babak Rasolzadeh, Stefan Carlsson
Venue: CVPR
Year: 2011

In this paper we address the problem of using boosting (e.g. AdaBoost [7]) to classify a target class with significant intra-class variation against a large background class. This situation occurs for example when we want to recognize a visual object class against all other image patches. The boosting algorithm produces a strong classifier, which is a linear combination of weak classifiers. We observe that we often have sets of weak classifiers that individually fire on many examples of the target class but never fire together on those examples (i.e. their outputs are anti-correlated on the target class). Motivated by this observation we suggest a family of derived weak classifiers, termed gated classifiers, that suppress such combinations of weak classifiers. Gated classifiers can be used on top of any original weak learner. We run experiments on two popular datasets, showing that our method reduces the required number of weak classifiers by almost an order of magnitude, which in turn yields faster detectors. We experiment on synthetic data showing that gated classifiers enables more complex distributions to be represented. We hope that gated classifiers will extend the usefulness of boosted classifier cascades [29].
On analyzing video with very small motions
Authors: Michael Dixon, Austin Abrams, Nathan Jacobs, Robert Pless
Venue: CVPR
Year: 2011

We characterize a class of videos consisting of very small but potentially complicated motions. We find that in these scenes, linear appearance variations have a direct relationship to scene motions. We show how to interpret appearance variations captured through a PCA decomposition of the image set as a scene-specific non-parametric motion basis. We propose fast, robust tools for dense flow estimates that are effective in scenes with small motions and potentially large image noise. We show example results in a variety of applications, including motion segmentation and long-term point tracking.
Sparsity-based image denoising via dictionary learning and structural clustering
Authors: Weisheng Dong, Xin Li 0005, Lei Zhang 0006, Guangming Shi
Venue: CVPR
Year: 2011

Where does the sparsity in image signals come from? Local and nonlocal image models have supplied complementary views toward the regularity in natural images - the former attempts to construct or learn a dictionary of basis functions that promotes the sparsity; while the latter connects the sparsity with the self-similarity of the image source by clustering. In this paper, we present a variational framework for unifying the above two views and propose a new denoising algorithm built upon clustering-based sparse representation (CSR). Inspired by the success of l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -optimization, we have formulated a double-header l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -optimization problem where the regularization involves both dictionary learning and structural structuring. A surrogate-function based iterative shrinkage solution has been developed to solve the double-header l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -optimization problem and a probabilistic interpretation of CSR model is also included. Our experimental results have shown convincing improvements over state-of-the-art denoising technique BM3D on the class of regular texture images. The PSNR performance of CSR denoising is at least comparable and often superior to other competing schemes including BM3D on a collection of 12 generic natural images.
Registration of camera captured documents under non-rigid deformation
Authors: Venkata Gopal Edupuganti, Vinayak A. Agarwal, Suryaprakash Kompalli
Venue: CVPR
Year: 2011

Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Given the registration parameters, layout of the template image is superimposed on the test document. Registration algorithms have been popular in applications, such as forms processing where the superimposed layout is used to extract relevant fields. Prior art has been designed to work with scanned documents under affine transformation. We find that the proliferation of camera captured images makes it necessary to address camera noise such as non-uniform lighting, clutter, and highly variable scale/resolution. The absence of a scan bed also leads to challenging non-rigid deformations being seen in paper images. Prior approaches in point pattern based registration like RANdom SAmple Consensus (RANSAC), and Thin Plate Spline-Robust Point Matching (TPS-RPM)form the basis of our work. We propose enhancements to these methods to enable registration of cell phone and camera captured documents under non-rigid transformations. We embed three novel aspects into the framework: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the regions of interest (ROI) to select only close by regions for matching, (iii) validation of the registration in RANSAC and TPS-RPM algorithms for non-rigid registration. We consider Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) as our features. Results are reported as comparing prior art with our method on a dataset that will be made publicly available.
Line-based relative pose estimation
Authors: Ali Elqursh, Ahmed M. Elgammal
Venue: CVPR
Year: 2011

We present an algorithm for calibrated camera relative pose estimation from lines. Given three lines with two of the lines parallel and orthogonal to the third we can compute the relative rotation between two images. We can also compute the relative translation from two intersection points. We also present a framework in which such lines can be detected. We evaluate the performance of the algorithm using synthetic and real data. The intended use of the algorithm is with robust hypothesize-and-test frameworks such as RANSAC. Our approach is suitable for urban and indoor environments where most lines are either parallel or orthogonal to each other.
Aggregating gradient distributions into intensity orders: A novel local image descriptor
Authors: Bin Fan 0001, Fuchao Wu, Zhanyi Hu
Venue: CVPR
Year: 2011

A novel local image descriptor is proposed in this paper, which combines intensity orders and gradient distributions in multiple support regions. The novelty lies in three aspects: 1) The gradient is calculated in a rotation invariant way in a given support region; 2) The rotation invariant gradients are adaptively pooled spatially based on intensity orders in order to encode spatial information; 3) Multiple support regions are used for constructing descriptor which further improves its discriminative ability. Therefore, the proposed descriptor encodes not only gradient information but also information about relative relationship of intensities as well as spatial information. In addition, it is truly rotation invariant in theory without the need of computing a dominant orientation which is a major error source of most existing methods, such as SIFT. Results on the standard Oxford dataset and 3D objects have shown a significant improvement over the state-of-the-art methods under various image transformations.
Heat-mapping: A robust approach toward perceptually consistent mesh segmentation
Authors: Yi Fang 0006, Mengtian Sun, Minhyong Kim, Karthik Ramani
Venue: CVPR
Year: 2011

3D mesh segmentation is a fundamental low-level task with applications in areas as diverse as computer vision, computer-aided design, bio-informatics, and 3D medical imaging. A perceptually consistent mesh segmentation (PCMS), as defined in this paper is one that satisfies 1) in-variance to isometric transformation of the underlying surface, 2) robust to the perturbations of the surface, 3) robustness to numerical noise on the surface, and 4) close conformation to human perception. We exploit the intelligence of the heat as a global structure-aware message on a meshed surface and develop a robust PCMS scheme, called Heat-Mapping based on the heat kernel. There are three main steps in Heat-Mapping. First, the number of the segments is estimated based on the analysis of the behavior of the Laplacian spectrum. Second, the heat center, which is defined as the most representative vertex on each segment, is discovered by a proposed heat center hunting algorithm. Third, a heat center driven segmentation scheme reveals the PCMS with a high consistency towards human perception. Extensive experimental results on various types of models verify the performance of Heat-Mapping with respect to the consistent segmentation of articulated bodies, the topological changes, and various levels of numerical noise.
sLLE: Spherical locally linear embedding with applications to tomography
Authors: Yi Fang 0006, Mengtian Sun, S. V. N. Vishwanathan, Karthik Ramani
Venue: CVPR
Year: 2011

The tomographic reconstruction of a planar object from its projections taken at random unknown view angles is a problem that occurs often in medical imaging. Therefore, there is a need to robustly estimate the view angles given random observations of the projections. The widely used locally linear embedding (LLE) technique provides nonlinear embedding of points on a flat manifold. In our case, the projections belong to a sphere. Therefore, we extend LLE and develop a spherical locally linear embedding (sLLE) algorithm, which is capable of embedding data points on a non-flat spherically constrained manifold. Our algorithm, sLLE, transforms the problem of the angle estimation to a spherically constrained embedding problem. It considers each projection as a high dimensional vector with dimensionality equal to the number of sampling points on the projection. The projections are then embedded onto a sphere, which parametrizes the projections with respect to view angles in a globally consistent manner. The image is reconstructed from parametrized projections through the inverse Radon transform. A number of experiments demonstrate that sLLE is particularly effective for the tomography application we consider. We evaluate its performance in terms of the computational efficiency and noise tolerance, and show that sLLE can be used to shed light on the other constrained applications of LLE.
Learning to recognize objects in egocentric activities
Authors: Alireza Fathi, Xiaofeng Ren, James M. Rehg
Venue: CVPR
Year: 2011

This paper addresses the problem of learning object models from egocentric video of household activities, using extremely weak supervision. For each activity sequence, we know only the names of the objects which are present within it, and have no other knowledge regarding the appearance or location of objects. The key to our approach is a robust, unsupervised bottom up segmentation method, which exploits the structure of the egocentric domain to partition each frame into hand, object, and background categories. By using Multiple Instance Learning to match object instances across sequences, we discover and localize object occurrences. Object representations are refined through transduction and object-level classifiers are trained. We demonstrate encouraging results in detecting novel object instances using models produced by weakly-supervised learning.
Geometric ℓp-norm feature pooling for image classification
Authors: Jiashi Feng, Bingbing Ni, Qi Tian 0001, Shuicheng Yan
Venue: CVPR
Year: 2011

Modern visual classification models generally include a feature pooling step, which aggregates local features over the region of interest into a statistic through a certain spatial pooling operation. Two commonly used operations are the average and max poolings. However, recent theoretical analysis has indicated that neither of these two pooling techniques may be qualified to be optimal. Besides, we further reveal in this work that more severe limitations of these two pooling methods are from the unrecoverable loss of the spatial information during the statistical summarization and the underlying over-simplified assumption about the feature distribution. We aim to address these inherent issues in this work and generalize previous pooling methods as follows. We define a weighted ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm spatial pooling function tailored for the class-specific feature spatial distribution. Moreover, a sensible prior for the feature spatial correlation is incorporated. Optimizing such pooling function towards optimal class separability yields a so-called geometric ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm pooling (GLP) method. The described GLP method is capable of preserving the class-specific spatial/geometric information in the pooled features and significantly boosts the discriminating capability of the resultant features for image classification. Comprehensive evaluations on several image benchmarks demonstrate that the proposed GLP method can boost the image classification performance with a single type of feature to outperform or be comparable with the state-of-the-arts.
Modelling composite shapes by Gibbs random fields
Authors: Boris Flach, Dmitrij Schlesinger
Venue: CVPR
Year: 2011

We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express spatial relations between shape parts and simple shapes simultaneously. This allows to model and recognise complex shapes as spatial compositions of simpler parts.
Predicting image matching using affine distortion models
Authors: Daniel Fleck, Zoran Duric
Venue: CVPR
Year: 2011

We propose a novel method for predicting whether an image taken from a given location will match an existing set of images. This problem appears prominently in image based localization and augmented reality applications where new images are matched to an existing set to determine location or add virtual information into a scene. Our process generates a spatial coverage map showing the confidence that images taken at specific locations will match an existing image set. A new way to measure distortion between images using affine models is introduced. The distortion measure is combined with existing machine learning and structure from motion techniques to create a matching confidence predictor. The predictor is used to generate the spatial coverage map and also compute which images in the original set are redundant and can be removed. Results are presented showing the predictor is more accurate than previously published approaches.
Functional categorization of objects using real-time markerless motion capture
Authors: Juergen Gall, Andrea Fossati, Luc Van Gool
Venue: CVPR
Year: 2011

Unsupervised categorization of objects is a fundamental problem in computer vision. While appearance-based methods have become popular recently, other important cues like functionality are largely neglected. Motivated by psycho logical studies giving evidence that human demonstration has a facilitative effect on categorization in infancy, we pro pose an approach for object categorization from depth video streams. To this end, we have developed a method for cap turing human motion in real-time. The captured data is then used to temporally segment the depth streams into actions. The set of segmented actions are then categorized in an un supervised manner, through a novel descriptor for motion capture data that is robust to subject variations. Further more, we automatically localize the object that is manipulated within a video segment, and categorize it using the corresponding action. For evaluation, we have recorded a dataset that comprises depth data with registered video sequences for 6 subjects, 13 action classes, and 174 object manipulations.
Inference for order reduction in Markov random fields
Authors: Andrew C. Gallagher, Dhruv Batra, Devi Parikh
Venue: CVPR
Year: 2011

This paper presents an algorithm for order reduction of factors in High-Order Markov Random Fields (HOMRFs). Standard techniques for transforming arbitrary high-order factors into pairwise ones have been known for a long time. In this work, we take a fresh look at this problem with the following motivation: It is important to keep in mind that order reduction is followed by an inference procedure on the order-reduced MRF. Since there are many possible ways of performing order reduction, a technique that generates “easier” pairwise inference problems is a better reduction. With this motivation in mind, we introduce a new algorithm called Order Reduction Inference (ORI) that searches over a space of order reduction methods to minimize the difficulty of the resultant pairwise inference problem. We set up this search problem as an energy minimization problem. We show that application of ORI for order reduction outperforms known order reduction techniques both in simulated problems and in real-world vision applications.
From region similarity to category discovery
Authors: Carolina Galleguillos, Brian McFee, Serge J. Belongie, Gert R. G. Lanckriet
Venue: CVPR
Year: 2011

The goal of object category discovery is to automatically identify groups of image regions which belong to some new, previously unseen category. This task is typically performed in a purely unsupervised setting, and as a result, performance depends critically upon accurate assessments of similarity between unlabeled image regions. To improve the accuracy of category discovery, we develop a novel multiple kernel learning algorithm based on structural SVM, which optimizes a similarity space for nearest-neighbor prediction. The optimized space is then used to cluster unlabeled data and identify new categories. Experimental results on the MSRC and PASCAL VOC2007 data sets indicate that using an optimized similarity metric can improve clustering for category discovery. Furthermore, we demonstrate that including both labeled and unlabeled training data when optimizing the similarity metric can improve the overall quality of the system.