OpenRaiser/Intern-Atlas huggingface dataset:
Generalized roof duality for pseudo-boolean optimization
Authors: Fredrik Kahl, Petter Strandmark
Venue: ICCV
Year: 2011

The number of applications in computer vision that model higher-order interactions has exploded over the last few years. The standard technique for solving such problems is to reduce the higher-order objective function to a quadratic pseudo-boolean function, and then use roof duality for obtaining a lower bound. Roof duality works by constructing the tightest possible lower-bounding submodular function, and instead of optimizing the original objective function, the relaxation is minimized. We generalize this idea to polynomials of higher degree, where quadratic roof duality appears as a special case. Optimal relaxations are defined to be the ones that give the maximum lower bound. We demonstrate that important properties such as persistency still hold and how the relaxations can be efficiently constructed for general cubic and quartic pseudo-boolean functions. From a practical point of view, we show that our relaxations perform better than state-of-the-art for a wide range of problems, both in terms of lower bounds and in the number of assigned variables.
Discovering object instances from scenes of Daily Living
Authors: Hongwen Kang, Martial Hebert, Takeo Kanade
Venue: ICCV
Year: 2011

We propose an approach to identify and segment objects from scenes that a person (or robot) encounters in Activities of Daily Living (ADL). Images collected in those cluttered scenes contain multiple objects. Each image provides only a partial, possibly very different view of each object. An object instance discovery program must be able to link pieces of visual information from multiple images and extract the consistent patterns.
Face reconstruction in the wild
Authors: Ira Kemelmacher-Shlizerman, Steven M. Seitz
Venue: ICCV
Year: 2011

We address the problem of reconstructing 3D face models from large unstructured photo collections, e.g., obtained by Google image search or from personal photo collections in iPhoto. This problem is extremely challenging due to the high degree of variability in pose, illumination, facial expression, non-rigid changes in face shape and reflectance over time and occlusions. In light of this extreme variability, no single reconstruction can be consistent with all of the images. Instead, we define as the goal of reconstruction to recover a model that is locally consistent with the image set. I.e., each local region of the model is consistent with a large set of photos, resulting in a model that captures the dominant trends in the input data for different parts of the face. Our approach leverages multi-image shading, but unlike traditional photometric stereo approaches, allows for changes in viewpoint and shape. We optimize over pose, shape, and lighting in an iterative approach that seeks to minimize the rank of the transformed images. This approach produces high quality shape models for a wide range of celebrities from photos available on the Internet.
Contour Code: Robust and efficient multispectral palmprint encoding for human recognition
Authors: Zohaib Khan 0001, Ajmal S. Mian, Yiqun Hu
Venue: ICCV
Year: 2011

We propose `Contour Code', a novel representation and binary hash table encoding for multispectral palmprint recognition. We first present a reliable technique for the extraction of a region of interest (ROI) from palm images acquired with non-contact sensors. The Contour Code representation is then derived from the Nonsubsampled Contourlet Transform. A uniscale pyramidal filter is convolved with the ROI followed by the application of a directional filter bank. The dominant directional subband establishes the orientation at each pixel and the index corresponding to this subband is encoded in the Contour Code representation. Unlike existing representations which extract orientation features directly from the palm images, the Contour Code uses a two stage filtering to extract robust orientation features. The Contour Code is binarized into an efficient hash table structure that only requires indexing and summation operations for simultaneous one-to-many matching with an embedded score level fusion of multiple bands. We quantitatively evaluate the accuracy of the ROI extraction by comparison with a manually produced ground truth. Multispectral palmprint verification results on the PolyU and CASIA databases show that the Contour Code achieves an EER reduction upto 50%, compared to state-of-the-art methods.
Similarity invariant classification of events by KL divergence minimization
Authors: Salman Khokhar, Imran Saleemi, Mubarak Shah
Venue: ICCV
Year: 2011

This paper proposes a novel method for recognition and classification of events represented by Mixture distributions of location and flow. The main idea is to classify observed events into semantically meaningful groups even when motion is observed from distinct viewpoints. Events in the proposed framework are modeled as motion patterns, which are represented by mixtures of multivariate Gaussians, and are obtained by hierarchical clustering of optical flow in the four dimensional space (x, y, u, v). Such motion patterns observed from varying viewpoints, and in distinct locations or datasets, can be compared using different families of divergences between statistical distributions, given that a transformation between the views is known. One of the major contributions of this paper is to compare and match two motion pattern mixture distributions by estimating the similarity transformation between them, that minimizes their Kullback-Leibler (KL) divergence. The KL divergence between Gaussian mixtures is approximated by Monte Carlo sampling, and the minimization is accomplished by employing an iterative nonlinear least squares estimation method, which bears close resemblance to the Iterative Closest Point (ICP) algorithm. We present a robust framework for matching of high-dimensional, sampled point sets representing statistical distributions, by defining similarity measures between them, for global energy minimization. The proposed approach is tested for classification of events observed across several datasets, captured from both static and moving cameras, involving real world pedestrian as well as vehicular motion. Encouraging results are obtained which demonstrate the feasibility and validity of the proposed approach.
A convex framework for image segmentation with moment constraints
Authors: Maria Klodt, Daniel Cremers
Venue: ICCV
Year: 2011

Convex relaxation techniques have become a popular approach to image segmentation as they allow to compute solutions independent of initialization to a variety of image segmentation problems. In this paper, we will show that shape priors in terms of moment constraints can be imposed within the convex optimization framework, since they give rise to convex constraints. In particular, the lower-order moments correspond to the overall volume, the centroid, and the variance or covariance of the shape and can be easily imposed in interactive segmentation methods. Respective constraints can be imposed as hard constraints or soft constraints. Quantitative segmentation studies on a variety of images demonstrate that the user can easily impose such constraints with a few mouse clicks, giving rise to substantial improvements of the resulting segmentation, and reducing the average segmentation error from 12% to 0:35%. GPU-based computation times of around 1 second allow for interactive segmentation.
Learning to cluster using high order graphical models with latent variables
Authors: Nikos Komodakis
Venue: ICCV
Year: 2011

This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem with latent variables, and applies a dual decomposition approach for training this model. The resulting framework allows learning a very broad class of distance functions, permits an automatic determination of the number of clusters during testing, and is also very efficient. As an additional contribution, we show how our method can be generalized to handle the training of a very broad class of important models in computer vision: arbitrary high-order latent CRFs. Experimental results verify its effectiveness.
Structured class-labels in random forests for semantic image labelling
Authors: Peter Kontschieder, Samuel Rota Bulò, Horst Bischof, Marcello Pelillo
Venue: ICCV
Year: 2011

In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of object classes in a given image. Different object class labels will not be randomly distributed over an image but usually form coherently labelled regions. In this work we provide a way to incorporate this topological information in the popular random forest framework for performing low-level, unary classification. Our paper has several contributions: First, we show how random forests can be augmented with structured label information. In the second part, we introduce a novel data splitting function that exploits the joint distributions observed in the structured label space for learning typical label transitions between object classes. Finally, we provide two possibilities for integrating the structured output predictions into concise, semantic labellings. In our experiments on the challenging MSRC and CamVid databases, we compare our method to standard random forest and conditional random field classification results.
Coherency Sensitive Hashing
Authors: Simon Korman, Shai Avidan
Venue: ICCV
Year: 2011

Coherency Sensitive Hashing (CSH) extends Locality Sensitivity Hashing (LSH) and PatchMatch to quickly find matching patches between two images. LSH relies on hashing, which maps similar patches to the same bin, in order to find matching patches. PatchMatch, on the other hand, relies on the observation that images are coherent, to propagate good matches to their neighbors, in the image plane. It uses random patch assignment to seed the initial matching. CSH relies on hashing to seed the initial patch matching and on image coherence to propagate good matches. In addition, hashing lets it propagate information between patches with similar appearance (i.e., map to the same bin). This way, information is propagated much faster because it can use similarity in appearance space or neighborhood in the image plane. As a result, CSH is at least three to four times faster than PatchMatch and more accurate, especially in textured regions, where reconstruction artifacts are most noticeable to the human eye. We verified CSH on a new, large scale, data set of 133 image pairs.
Random ensemble metrics for object recognition
Authors: Tatsuo Kozakaya, Satoshi Ito, Susumu Kubota
Venue: ICCV
Year: 2011

This paper presents a novel and generic approach for metric learning, random ensemble metrics (REMetric). To improve generalization performance, we introduce the concept of ensemble learning to the metric learning scheme. Unlike previous methods, our method does not optimize the global objective function for the whole training data. It learns multiple discriminative projection vectors obtained from linear support vector machines (SVM) using randomly subsampled training data. The final metric matrix is then obtained by integrating these vectors. As a result of using SVM, the learned metric has an excellent scalability for the dimensionality of features. Therefore, it does not require any prior dimensionality reduction techniques such as PCA. Moreover, our method allows us to unify dimensionality reduction and metric learning by controlling the number of the projection vectors. We demonstrate through experiments, that our method can avoid overfitting even though a relatively small number of training data is provided. The experiments are performed with three different datasets; the Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset, the Labeled Face in the Wild (LFW) dataset and the Oxford 102 category flower dataset. The results show that our method achieves equivalent or superior performance compared to existing state-of-the-art metric learning methods.
Modeling spatial layout with fisher vectors for image categorization
Authors: Josip Krapac, Jakob Verbeek, Frédéric Jurie
Venue: ICCV
Year: 2011

We introduce an extension of bag-of-words image representations to encode spatial layout. Using the Fisher kernel framework we derive a representation that encodes the spatial mean and the variance of image regions associated with visual words. We extend this representation by using a Gaussian mixture model to encode spatial layout, and show that this model is related to a soft-assign version of the spatial pyramid representation. We also combine our representation of spatial layout with the use of Fisher kernels to encode the appearance of local features. Through an extensive experimental evaluation, we show that our representation yields state-of-the-art image categorization results, while being more compact than spatial pyramid representations. In particular, using Fisher kernels to encode both appearance and spatial layout results in an image representation that is computationally efficient, compact, and yields excellent performance while using linear classifiers.
Maximizing all margins: Pushing face recognition with Kernel Plurality
Authors: Ritwik Kumar, Arunava Banerjee, Baba C. Vemuri, Hanspeter Pfister
Venue: ICCV
Year: 2011

We present two theses in this paper: First, performance of most existing face recognition algorithms improves if instead of the whole image, smaller patches are individually classified followed by label aggregation using voting. Second, weighted plurality <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> voting outperforms other popular voting methods if the weights are set such that they maximize the victory margin for the winner with respect to each of the losers. Moreover, this can be done while taking higher order relationships among patches into account using kernels. We call this scheme Kernel Plurality. We verify our proposals with detailed experimental results and show that our framework with Kernel Plurality improves the performance of various face recognition algorithms beyond what has been previously reported in the literature. Furthermore, on five different benchmark datasets - Yale A, CMU PIE, MERL Dome, Extended Yale B and Multi-PIE, we show that Kernel Plurality in conjunction with recent face recognition algorithms can provide state-of-the-art results in terms of face recognition rates.
Realtime multibody visual SLAM with a smoothly moving monocular camera
Authors: Abhijit Kundu, K. Madhava Krishna, C. V. Jawahar
Venue: ICCV
Year: 2011

This paper presents a realtime, incremental multibody visual SLAM system that allows choosing between full 3D reconstruction or simply tracking of the moving objects. Motion reconstruction of dynamic points or objects from a monocular camera is considered very hard due to well known problems of observability. We attempt to solve the problem with a Bearing only Tracking (BOT) and by integrating multiple cues to avoid observability issues. The BOT is accomplished through a particle filter, and by integrating multiple cues from the reconstruction pipeline. With the help of these cues, many real world scenarios which are considered unobservable with a monocular camera is solved to reasonable accuracy. This enables building of a unified dynamic 3D map of scenes involving multiple moving objects. Tracking and reconstruction is preceded by motion segmentation and detection which makes use of efficient geometric constraints to avoid difficult degenerate motions, where objects move in the epipolar plane. Results reported on multiple challenging real world image sequences verify the efficacy of the proposed framework.
Markov Random Field-based fitting of a subdivision-based geometric atlas
Authors: Uday Kurkure, Yen H. Le, Nikos Paragios, Tao Ju 0001, James P. Carson, Ioannis A. Kakadiaris
Venue: ICCV
Year: 2011

An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh's geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.
Generalized background subtraction based on hybrid inference by belief propagation and Bayesian filtering
Authors: Suha Kwak, Taegyu Lim, Woonhyun Nam, Bohyung Han, Joon Hee Han
Venue: ICCV
Year: 2011

We propose a novel background subtraction algorithm for the videos captured by a moving camera. In our technique, foreground and background appearance models in each frame are constructed and propagated sequentially by Bayesian filtering. We estimate the posterior of appearance, which is computed by the product of the image likelihood in the current frame and the prior appearance propagated from the previous frame. The motion, which transfers the previous appearance models to the current frame, is estimated by nonparametric belief propagation; the initial motion field is obtained by optical flow and noisy and incomplete motions are corrected effectively through the inference procedure. Our framework is represented by a graphical model, where the sequential inference of motion and appearance is performed by the combination of belief propagation and Bayesian filtering. We compare our algorithm with the existing state-of-the-art technique and evaluate its performance quantitatively and qualitatively in several challenging videos.
Tracking by Sampling Trackers
Authors: Junseok Kwon, Kyoung Mu Lee
Venue: ICCV
Year: 2011

We propose a novel tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation. To do this, our method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the basic important components of visual trackers. Then, the sampled trackers run in parallel and interact with each other while covering various target variations efficiently. The experiment demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments and outperforms the state-of-the-art tracking methods.
Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces
Authors: Rongjie Lai, Yonggang Shi, Nancy L. Sicotte, Arthur W. Toga
Venue: ICCV
Year: 2011

Corpus callosum (CC) is an important structure in human brain anatomy. In this work, we propose a fully automated and robust approach to extract corpus callosum from T1-weighted structural MR images. The novelty of our method is composed of two key steps. In the first step, we find an initial guess for the curve representation of CC by using the zero level set of the first nontrivial Laplace-Beltrami (LB) eigenfunction on the white matter surface. In the second step, the initial curve is deformed toward the final solution with a geodesic curvature flow on the white matter surface. For numerical solution of the geodesic curvature flow on surfaces, we represent the contour implicitly on a triangular mesh and develop efficient numerical schemes based on finite element method. Because our method depends only on the intrinsic geometry of the white matter surface, it is robust to orientation differences of the brain across population. In our experiments, we validate the proposed algorithm on 32 brains from a clinical study of multiple sclerosis disease and demonstrate that the accuracy of our results.
Cluster-based color space optimizations
Authors: Cheryl Lau, Wolfgang Heidrich, Rafal Mantiuk
Venue: ICCV
Year: 2011

Transformations between different color spaces and gamuts are ubiquitous operations performed on images. Often, these transformations involve information loss, for example when mapping from color to grayscale for printing, from multispectral or multiprimary data to tristimulus spaces, or from one color gamut to another. In all these applications, there exists a straightforward “natural” mapping from the source space to the target space, but the mapping is not bijective, resulting in information loss due to metamerism and similar effects. We propose a cluster-based approach for optimizing the transformation for individual images in a way that preserves as much of the information as possible from the source space while staying as faithful as possible to the natural mapping. Our approach can be applied to a host of color transformation problems including color to gray, gamut mapping, conversion of multispectral and multiprimary data to tristimulus colors, and image optimization for color deficient viewers.
Key-segments for video object segmentation
Authors: Yong Jae Lee, Jaechul Kim, Kristen Grauman
Venue: ICCV
Year: 2011

We present an approach to discover and segment foreground object(s) in video. Given an unannotated video sequence, the method first identifies object-like regions in any frame according to both static and dynamic cues. We then compute a series of binary partitions among those candidate “key-segments” to discover hypothesis groups with persistent appearance and motion. Finally, using each ranked hypothesis in turn, we estimate a pixel-level object labeling across all frames, where (a) the foreground likelihood depends on both the hypothesis's appearance as well as a novel localization prior based on partial shape matching, and (b) the background likelihood depends on cues pulled from the key-segments' (possibly diverse) surroundings observed across the sequence. Compared to existing methods, our approach automatically focuses on the persistent foreground regions of interest while resisting oversegmentation. We apply our method to challenging benchmark videos, and show competitive or better results than the state-of-the-art.