OpenRaiser/Intern-Atlas huggingface dataset:
Optical flow estimation on coarse-to-fine region-trees using discrete optimization
Authors: Cheng Lei, Yee-Hong Yang
Venue: ICCV
Year: 2009

In this paper, we propose a new region-based method for accurate motion estimation using discrete optimization. In particular, the input image is represented as a tree of over-segmented regions and the optical flow is estimated by optimizing an energy function defined on such a region-tree using dynamic programming. To accommodate the sampling-inefficiency problem intrinsic to discrete optimization compared to the continuous optimization based methods, both spatial and solution domain coarse-to-fine (C2F) strategies are used. That is, multiple region-trees are built using different over-segmentation granularities. Starting from a global displacement label discretization, optical flow estimation on the coarser level region-tree is used for defining region-wise finer displacement samplings for finer level region-trees. Furthermore, cross-checking based occlusion detection and correction and continuous optimization are also used to improve accuracy. Extensive experiments using the Middlebury benchmark datasets have shown that our proposed method can produce top-ranking results.
The Swap and Expansion moves revisited and fused
Authors: Ido Leichter
Venue: ICCV
Year: 2009

Many solutions to computer vision and image processing problems involve the minimization of multi-label energy functions with up to K variables in each term. In the minimization process, Swap and Expansion are two commonly used moves. This paper re-derives the optimal swap and expansion moves for K = 2 in a short manner by using the original pseudo-Boolean quadratic function minimization method as a “black box”. It is then revealed that the found minima w.r.t. expansion moves are in fact also minima w.r.t. swap moves. This process is repeated for K = 3. The minima-related result is extended to all functions under the condition that they are reduced into submodular ones, which makes it applicable to all expansion algorithms. These may explain the prevalent impression that expansion algorithms are more effective than swap algorithms. To make the search space larger, Exwap - a generalization of the expansion and the swap moves - is introduced. Efficient algorithms for minimizing w.r.t. it for K = 2 (including a `truncation' procedure), K = 3, and the Pn Potts model are provided. The move is capable of reaching lower energies than those reached by the expansion algorithm, as demonstrated for several benchmark problems.
Boundary ownership by lifting to 2.1D
Authors: Ido Leichter, Michael Lindenbaum
Venue: ICCV
Year: 2009

This paper addresses the “boundary ownership” problem, also known as the figure/ground assignment problem. Estimating boundary ownerships is a key step in perceptual organization: it allows higher-level processing to be applied on non-accidental shapes corresponding to figural regions. Existing methods for estimating the boundary ownerships for a given set of boundary curves model the probability distribution function (PDF) of the binary figure/ground random variables associated with the curves. Instead of modeling this PDF directly, the proposed method uses the 2.1D model: it models the PDF of the ordinal depths of the image segments enclosed by the curves. After this PDF is maximized, the boundary ownership of a curve is determined according to the ordinal depths of the two image segments it abuts. This method has two advantages: first, boundary ownership configurations inconsistent with every depth ordering (and thus very likely to be incorrect) are eliminated from consideration; second, it allows for the integration of cues related to image segments (not necessarily adjacent) in addition to those related to the curves. The proposed method models the PDF as a conditional random field (CRF) conditioned on cues related to the curves, T-junctions, and image segments. The CRF is formulated using learnt non-parametric distributions of the cues. The method significantly improves the currently achieved figure/ground assignment accuracy, with 20.7% fewer errors in the Berkeley Segmentation Dataset.
Convex optimization for multi-class image labeling with a novel family of total variation based regularizers
Authors: Jan Lellmann, Florian Becker, Christoph Schnörr
Venue: ICCV
Year: 2009

We introduce a linearly weighted variant of the total variation for vector fields in order to formulate regularizers for multi-class labeling problems with non-trivial interclass distances. We characterize the possible distances, show that Euclidean distances can be exactly represented, and review some methods to approximate non-Euclidean distances in order to define novel total variation based regularizers. We show that the convex relaxed problem can be efficiently optimized to a prescribed accuracy with optimality certificates using Nesterov's method, and evaluate and compare our approach on several synthetical and real-world examples.
Multiscale symmetric part detection and grouping
Authors: Alex Levinshtein, Sven J. Dickinson, Cristian Sminchisescu
Venue: ICCV
Year: 2009

Skeletonization algorithms typically decompose an object's silhouette into a set of symmetric parts, offering a powerful representation for shape categorization. However, having access to an object's silhouette assumes correct figure-ground segmentation, leading to a disconnect with the mainstream categorization community, which attempts to recognize objects from cluttered images. In this paper, we present a novel approach to recovering and grouping the symmetric parts of an object from a cluttered scene. We begin by using a multiresolution superpixel segmentation to generate medial point hypotheses, and use a learned affinity function to perceptually group nearby medial points likely to belong to the same medial branch. In the next stage, we learn higher granularity affinity functions to group the resulting medial branches likely to belong to the same object. The resulting framework yields a skeletal approximation that's free of many of the instabilities plaguing traditional skeletons. More importantly, it doesn't require a closed contour, enabling the application of skeleton-based categorization systems to more realistic imagery
Landmark classification in large-scale image collections
Authors: Yunpeng Li 0002, David J. Crandall, Daniel P. Huttenlocher
Venue: ICCV
Year: 2009

With the rise of photo-sharing websites such as Facebook and Flickr has come dramatic growth in the number of photographs online. Recent research in object recognition has used such sites as a source of image data, but the test images have been selected and labeled by hand, yielding relatively small validation sets. In this paper we study image classification on a much larger dataset of 30 million images, including nearly 2 million of which have been labeled into one of 500 categories. The dataset and categories are formed automatically from geotagged photos from Flickr, by looking for peaks in the spatial geotag distribution corresponding to frequently-photographed landmarks. We learn models for these landmarks with a multiclass support vector machine, using vector-quantized interest point descriptors as features. We also explore the non-visual information available on modern photo-sharing sites, showing that using textual tags and temporal constraints leads to significant improvements in classification rate. We find that in some cases image features alone yield comparable classification accuracy to using text tags as well as to the performance of human observers.
Constrained clustering by spectral kernel learning
Authors: Zhenguo Li, Jianzhuang Liu
Venue: ICCV
Year: 2009

Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify some pairs of objects from the same cluster or not. The main idea is to design a kernel to respect both the proximity structure of the data and the given pairwise constraints. We propose a spectral kernel learning framework and formulate it as a convex quadratic program, which can be optimally solved efficiently. Our framework enjoys several desirable features: 1) it is applicable to multi-class problems; 2) it can handle both must-link and cannot-link constraints; 3) it can propagate pairwise constraints effectively; 4) it is scalable to large-scale problems; and 5) it can handle weighted pairwise constraints. Extensive experiments have demonstrated the superiority of the proposed approach.
Image segmentation with simultaneous illumination and reflectance estimation: An energy minimization approach
Authors: Chunming Li, Fang Li, Chiu-Yen Kao, Chenyang Xu 0001
Venue: ICCV
Year: 2009

Spatial intensity variations caused by illumination changes have been a challenge for image segmentation and many other computer vision tasks. This paper presents a novel method for image segmentation with simultaneous estimation of illumination and reflectance images. The proposed method is based on the composition of an observed scene image with an illumination component and a reflectance component, known as intrinsic images. We define an energy functional in terms of an illumination image, the membership functions of the regions, and the corresponding reflectance constants of the regions in the scene. This energy is convex in each of its variables. By minimizing the energy, image segmentation result is obtained in the form of the membership functions of the regions. The illumination and reflectance components of the observed image are estimated simultaneously as the result of energy minimization. With illumination taken into account, the proposed method is able to segment images with non-uniform intensities caused by spatial variations in illumination. Comparisons with the state-of-the-art piecewise smooth model demonstrate the superior performance of our method.
Globally optimal affine epipolar geometry from apparent contours
Authors: Gang Li, Yanghai Tsin
Venue: ICCV
Year: 2009

We study the problem of estimating the epipolar geometry from apparent contours of smooth curved surfaces with affine camera models. Since apparent contours are viewpoint dependent, the only true image correspondences are projections of the frontier points, i.e., surface points whose tangent planes are also their epipolar planes. However, frontier points are unknown a priori and must be estimated simultaneously with epipolar geometry. Previous approaches to this problem adopt local greedy search methods which are sensitive to initialization, and may get trapped in local minima. We propose the first algorithm that guarantees global optimality for this problem. We first reformulate the problem using a separable form that allows us to search effectively in a 2D space, instead of on a 5D hypersphere in the classical formulation. Next, in a branch-and-bound algorithm we introduce a novel lower bounding function through interval matrix analysis. Experimental results on both synthetic and real scenes demonstrate that the proposed method is able to quickly obtain the optimal solution.
Context by region ancestry
Authors: Joseph J. Lim, Pablo Arbeláez, Chunhui Gu, Jitendra Malik
Venue: ICCV
Year: 2009

In this paper, we introduce a new approach for modeling visual context. For this purpose, we consider the leaves of a hierarchical segmentation tree as elementary units. Each leaf is described by features of its ancestral set, the regions on the path linking the leaf to the root. We construct region trees by using a high-performance segmentation method. We then learn the importance of different descriptors (e.g. color, texture, shape) of the ancestors for classification. We report competitive results on the MSRC segmentation dataset and the MIT scene dataset, showing that region ancestry efficiently encodes information about discriminative parts, objects and scenes.
Simultaneous camera pose and correspondence estimation in cornerless images
Authors: Wen-Yan Lin, Guo Dong, Ping Tan, Loong Fah Cheong, Chye Hwang Yan
Venue: ICCV
Year: 2009

We propose an algorithm which can jointly estimate camera pose and point set registration. Given point sets from two views of a stationary scene, our algorithm registers the point sets while retaining internal scene structure. It simultaneously ensures that the resultant registration is consistent with that of a moving camera viewing a static scene (adheres to some epipolar constraint). Our statistical formulation can incorporate but does not necessarily require additional constraints such as brightness constancy and high dimensional point descriptors such as SIFT. We show that our algorithm is stable over a variety of scenes and offers a pose from edge system which handles currently difficult structure from motion scenes more robustly.
Recognizing actions by shape-motion prototype trees
Authors: Zhe Lin 0001, Zhuolin Jiang, Larry S. Davis
Venue: ICCV
Year: 2009

A prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, first, an action prototype tree is learned in a joint shape and motion space via hierarchical k-means clustering; then a lookup table of prototype-to-prototype distances is generated. During testing, based on a joint likelihood model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint likelihood, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance matrices used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in very challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 91.07% on a large gesture dataset (with dynamic backgrounds), 100% on the Weizmann action dataset and 95.77% on the KTH action dataset.
Efficient discriminative local learning for object recognition
Authors: Yen-Yu Lin, Jyun-Fan Tsai, Tyng-Luh Liu
Venue: ICCV
Year: 2009

Although object recognition methods based on local learning can reasonably resolve the difficulties caused by the large variations in images from the same category, the high risk of overfitting and the heavy computational cost in training numerous local models (classifiers or distance functions) often limit their applicability. To address these two unpleasant issues, we cast the multiple, independent training processes of local models as a correlative multi-task learning problem, and design a new boosting algorithm to accomplish it. Specifically, we establish a parametric space where these local models lie and spread as a manifold-like structure, and use boosting to perform local model training by completing the manifold embedding. Via sharing the common embedding space, the learning of each local model can be properly regularized by the extra knowledge from other models, while the training time is also significantly reduced. Experimental results on two benchmark datasets, Caltech-101 and VOC 2007, support that our approach not only achieves promising recognition rates but also gives a two order speed-up in realizing local learning.
A robust boosting tracker with minimum error bound in a co-training framework
Authors: Rong Liu, Jian Cheng 0001, Hanqing Lu
Venue: ICCV
Year: 2009

The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tracking, and some online and semi-supervised algorithms are developed to handle these difficulties. In this paper, we consider tracking as a classification problem and present a novel tracking method based on boosting in a co-training framework. The proposed tracker can be online updated and boosted with multi-view weak hypothesis. The most important contribution of this paper is that we find a boosting error upper bound in a co-training framework to guide the novel tracker construction. In theory, the proposed tracking method is proved to minimize this error bound. In experiments, the accuracy rate of foreground/ background classification and the tracking results are both served as evaluation metrics. Experimental results show good performance of proposed novel tracker on challenging sequences.
Tensor completion for estimating missing values in visual data
Authors: Ji Liu 0002, Przemyslaw Musialski, Peter Wonka, Jieping Ye
Venue: ICCV
Year: 2009

In this paper we propose an algorithm to estimate missing values in tensors of visual data. The values can be missing due to problems in the acquisition process, or because the user manually identified unwanted outliers. Our algorithm works even with a small amount of samples and it can propagate structure to fill larger missing regions. Our methodology is built on recent studies about matrix completion using the matrix trace norm. The contribution of our paper is to extend the matrix case to the tensor case by laying out the theoretical foundations and then by building a working algorithm. First, we propose a definition for the tensor trace norm, that generalizes the established definition of the matrix trace norm. Second, similar to matrix completion, the tensor completion is formulated as a convex optimization problem. Unfortunately, the straightforward problem extension is significantly harder to solve than the matrix case because of the dependency among multiple constraints. To tackle this problem, we employ a relaxation technique to separate the dependant relationships and use the block coordinate descent (BCD) method to achieve a globally optimal solution. Our experiments show potential applications of our algorithm and the quantitative evaluation indicates that our method is more accurate and robust than heuristic approaches.
Simultaneous alignment and clustering for an image ensemble
Authors: Xiaoming Liu 0002, Yan Tong, Frederick W. Wheeler
Venue: ICCV
Year: 2009

Joint alignment for an image ensemble can rectify images in the spatial domain such that the aligned images are as similar to each other as possible. This important technology has been applied to various object classes and medical applications. However, previous approaches to joint alignment work on an ensemble of a single object class. Given an ensemble with multiple object classes, we propose an approach to automatically and simultaneously solve two problems, image alignment and clustering. Both the alignment parameters and clustering parameters are formulated into a unified objective function, whose optimization leads to an unsupervised joint estimation approach. It is further extended to semi-supervised simultaneous estimation where a few labeled images are provided. Extensive experiments on diverse real-world databases demonstrate the capabilities of our work on this challenging problem.
Unlabeled data improvesword prediction
Authors: Nicolas Loeff, Ali Farhadi, Ian Endres, David A. Forsyth
Venue: ICCV
Year: 2009

Labeling image collections is a tedious task, especially when multiple labels have to be chosen for each image. In this paper we introduce a new framework that extends state of the art models in word prediction to incorporate information from unlabeled examples, using manifold regularization. To the best of our knowledge this is the first semi-supervised multi-task model used in vision problems. The new model can be solved using gradient descent and is fast and efficient. We show remarkable improvements for cases with few labeled examples for challenging multi-task learning problems in vision (predicting words for images and attributes for objects).
Modelling activity global temporal dependencies using Time Delayed Probabilistic Graphical Model
Authors: Chen Change Loy, Tao Xiang 0002, Shaogang Gong
Venue: ICCV
Year: 2009

We present a novel approach for detecting global behaviour anomalies in multiple disjoint cameras by learning time delayed dependencies between activities cross camera views. Specifically, we propose to model multi-camera activities using a Time Delayed Probabilistic Graphical Model (TD-PGM) with different nodes representing activities in different semantically decomposed regions from different camera views, and the directed links between nodes encoding causal relationships between the activities. A novel two-stage structure learning algorithm is formulated to learn globally optimised time-delayed dependencies. A new cumulative abnormality score is also introduced to replace the conventional log-likelihood score for gaining significantly more robust and reliable real-time anomaly detection. The effectiveness of the proposed approach is validated using a camera network installed at a busy underground station.
Hierarchical learning for tubular structure parsing in medical imaging: A study on coronary arteries using 3D CT Angiography
Authors: Le Lu 0001, Jinbo Bi, Shipeng Yu, Zhigang Peng, Arun Krishnan, Xiang Sean Zhou
Venue: ICCV
Year: 2009

Automatic coronary artery centerline extraction from 3D CT Angiography (CTA) has significant clinical importance for diagnosis of atherosclerotic heart disease. The focus of past literature is dominated by segmenting the complete coronary artery system as trees by computer. Though the labeling of different vessel branches (defined by their medical semantics) is much needed clinically, this task has been performed manually. In this paper, we propose a hierarchical machine learning approach to tackle the problem of tubular structure parsing in medical imaging. It has a progressive three-tiered classification process at volumetric voxel level, vessel segment level, and inter-segment level. Generative models are employed to project from low-level, ambiguous data to class-conditional probabilities; and discriminative classifiers are trained on the upper-level structural patterns of probabilities to label and parse the vessel segments. Our method is validated by experiments of detecting and segmenting clinically defined coronary arteries, from the initial noisy vessel segment networks generated by low-level heuristics-based tracing algorithms. The proposed framework is also generically applicable to other tubular structure parsing tasks.
Color constancy using 3D scene geometry
Authors: Rui Lu, Arjan Gijsenij, Theo Gevers, Vladimir Nedovic, De Xu, Jan-Mark Geusebroek
Venue: ICCV
Year: 2009

The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions such as the grey-world and white patch assumptions.
Shape guided contour grouping with particle filters
Authors: ChengEn Lu, Longin Jan Latecki, Nagesh Adluru, Xingwei Yang, Haibin Ling
Venue: ICCV
Year: 2009

We propose a novel framework for contour based object detection and recognition, which we formulate as a joint contour fragment grouping and labeling problem. For a given set of contours of model shapes, we simultaneously perform selection of relevant contour fragments in edge images, grouping of the selected contour fragments, and their matching to the model contours. The inference in all these steps is performed using particle filters (PF) but with static observations. Our approach needs one example shape per class as training data. The PF framework combined with decomposition of model contour fragments to part bundles allows us to implement an intuitive search strategy for the target contour in a clutter of edge fragments. First a rough sketch of the model shape is identified, followed by fine tuning of shape details. We show that this framework yields not only accurate object detections but also localizations in real cluttered images.