OpenRaiser/Intern-Atlas huggingface dataset:
Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking
Authors: Zhaozheng Yin, Robert T. Collins
Venue: CVPR
Year: 2008

Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets.
Distributed data association and filtering for multiple target tracking
Authors: Ting Yu 0003, Ying Wu 0001, Nils Krahnstoever, Peter H. Tu
Venue: CVPR
Year: 2008

This paper presents a novel distributed framework for multi-target tracking with an efficient data association computation. A decentralized representation of trackers’ motion and association variables is adopted. Considering the interleaved nature of data association and tracker filtering, the multi-target tracking is formulated as a missing data problem, and the solution is found by the proposed variational EM algorithm. We analytically show that 1) the posteriori distributions of trackers’ motions (the real interests in terms of tracking applications) can be effectively computed in the E-step of the EM iterations, and 2) the solution of trackers’ association variables can be pursued under a derived graph-based discrete optimization formulation, thus efficiently estimated in the M-step by the recently emerging graph optimization algorithms. The proposed approach is very general such that sophisticated data association priori and likelihood function can be easily incorporated. This general framework is tested with both simulation data and real world surveillance video. The reported qualitative and quantitative studies verify the effectiveness and low computational cost of the algorithm.
Mining compositional features for boosting
Authors: Junsong Yuan 0001, Jiebo Luo 0001, Ying Wu 0001
Venue: CVPR
Year: 2008

The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the training error during the boosting process. Therefore, when constructing the weak classifier pool, we prefer the quality rather than the quantity of the weak classifiers. In this paper, we present a data mining-driven approach to discovering compositional features from a given and possibly small feature pool. Compared with individual features (e.g. weak decision stumps) which are of limited discriminative ability, the mined compositional features have guaranteed power in terms of the descriptive and discriminative abilities, as well as bounded training error. To cope with the combinatorial cost of discovering compositional features, we apply data mining methods (frequent itemset mining) to efficiently find qualified compositional features of any possible order. These weak classifiers are further combined through a multi-class AdaBoost method for final multi-class classification. Experiments on a challenging 10-class event recognition problem show that boosting compositional features can lead to faster decrease of training error and significantly higher accuracy compared to conventional boosting decision stumps.
Multiplicative kernels: Object detection, segmentation and pose estimation
Authors: Quan Yuan, Ashwin Thangali, Vitaly Ablavsky, Stan Sclaroff
Venue: CVPR
Year: 2008

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. Detector training can be accomplished via standard SVM learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the foreground parameters are provided in training, the detectors can also produce parameter estimate. When the foreground object masks are provided in training, the detectors can also produce object segmentation. The advantages of our method over past methods are demonstrated on data sets of human hands and vehicles.
Boundary snapping for robust image cutouts
Authors: Eyal Zadicario, Shai Avidan, Alon Shmueli, Daniel Cohen-Or
Venue: CVPR
Year: 2008

Boundary snapping is an interactive image cutout algorithm that requires a small number of user supplied control points, or landmarks, to infer the cutout contour. The key idea is to match the appearance of all points along the desired contour to the landmark points, where appearance is given by an intensity profile perpendicular to the boundary. An optimization process attempts to find a contour that maximizes the similarity score of its points with the landmarks. This approach works well in the typical case where the foreground and background differ in appearance, as well as in challenging cases where the subject is clearly perceived, but the regions on both sides of the boundary are similar and cannot be easily discriminated. By enabling the user to define the boundary points directly, the technique is not limited to boundaries that necessarily have to be the most salient or high gradient feature in the region. It can also be used for margin cutout around the boundary. The use of multiple control points along the boundary can handle spatially varying attributes as both foreground and background may change in appearance along the boundary. The final result is accurate, because it allows the user to enforce hard constraints on the boundary directly, at the expense of moderate user labor in positioning the landmark points. Finally, the algorithm is fast, works on a variety of images, and handles situations where the boundary is not obvious.
Multi-label image segmentation via point-wise repetition
Authors: Gang Zeng, Luc Van Gool
Venue: CVPR
Year: 2008

Bottom-up segmentation tends to rely on local features. Yet, many natural and man-made objects contain repeating elements. Such structural and more spread-out features are important cues for segmentation but are more difficult to exploit. The difficulty also comes from the fact that repetition need not be perfect, and will actually rather be partial, approximate, or both in most cases. This paper presents a multi-label image segmentation algorithm that processes a single input image and efficiently discovers and exploits repeating elements without any prior knowledge about their shape, color or structure. The algorithm spells out the interplay between segmentation and repetition detection. The key of our approach is a novel, point-wise concept of repetition. This is defined by point-wise mutual information and locally compares certain neighborhoods to accumulate evidence. This point-wise repetition measure naturally handles imperfect repetitions, and the parts with inconsistent appearances are recognized and assigned with low scores. An energy functional is proposed to include the point-wise repetition into the image segmentation process, which takes the form of a graph-cut minimization. Real scene images demonstrate the ability of our algorithm to handle partial and approximate repetition.
Joint multi-label multi-instance learning for image classification
Authors: Zheng-Jun Zha, Xian-Sheng Hua 0001, Tao Mei 0001, Jingdong Wang 0001, Guo-Jun Qi, Zengfu Wang
Venue: CVPR
Year: 2008

In real world, an image is usually associated with multiple labels which are characterized by different regions in the image. Thus image classification is naturally posed as both a multi-label learning and multi-instance learning problem. Different from existing research which has considered these two problems separately, we propose an integrated multi-label multi-instance learning (MLMIL) approach based on hidden conditional random fields (HCRFs), which simultaneously captures both the connections between semantic labels and regions, and the correlations among the labels in a single formulation. We apply this MLMIL framework to image classification and report superior performance compared to key existing approaches over the MSR Cambridge (MSRC) and Corel data sets.
Adaptive region intensity based rigid ultrasound and CT image registration
Authors: Zhijun Zhang 0001
Venue: CVPR
Year: 2008

Rigid registration of intraoperative ultrasound (US) and CT is an important technique to provide real-time guidance for preoperative images and models. Due to the speckle noise and artefacts in US images, accurate registration of CT and US is still a challenging problem. We propose an adaptive region intensity based CT and US registration method. The registration is initialized by matching the distinctive regions of CT and US images. Then the registration is a multistage process in which the regions in US used will be adaptively updated at each stage. The registration problem is considered as a global similarity energy optimization and high local statistical dependency regions selection process. Performances of our method and other intensity based method are evaluated with simulated and real datasets. Experiments results show the improvement of our registration method in robustness and accuracy.
Learning-based face hallucination in DCT domain
Authors: Wei Zhang 0021, Wai-kuen Cham
Venue: CVPR
Year: 2008

In this paper, we propose a novel learning-based face hallucination framework built in DCT domain, which can recover the high-resolution face image from a single low-resolution one. Unlike most previous learning-based work, our approach addresses the face hallucination problem from a different angle. In details, the problem is formulated as inferring DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Experimental results show that DC coefficients can be estimated fairly accurately by simple interpolation-based methods. AC coefficients, which contain the information of local features of face image, cannot be estimated well using interpolation. We propose a method to infer AC coefficients by introducing an efficient learning-based inference model. Moreover, the proposed framework can lead to significant savings in memory and computation cost since the redundancy of the training set is reduced a lot by clustering. Experimental results demonstrate that our approach is very effective to produce hallucinated face images with high quality.
Inverse-polar ray projection for recovering projective transformations
Authors: Yun Zhang, Henry Chu
Venue: CVPR
Year: 2008

A ray projection in the inverse-polar space is proposed for recovering a projective transformation between two segmented images. The images are converted from their original Cartesian space to the inverse-polar space. Then, the two ray projections—one shift-invariant and the other shift-sensitive—of the inverse-polar images are computed to create two sets of data. Based on the obtained projection data, a two-step strategy is employed to recover the projective transformation. In the first step, the shift-invariant data are used to recover the four affine parameters. In the second step, the shift-sensitive data are used to recover the two projective parameters. The remaining two translation-related parameters are recovered in, e.g., an exhaustive search combined with the two-step recovery strategy. The proposed approach has been tested successfully to recover a variety of projective transformations between real images.
Sequential particle swarm optimization for visual tracking
Authors: Xiaoqin Zhang 0002, Weiming Hu 0004, Stephen J. Maybank, Xi Li 0001, Mingliang Zhu
Venue: CVPR
Year: 2008

Visual tracking usually involves an optimization process for estimating the motion of an object from measured images in a video sequence. In this paper, a new evolutionary approach, PSO (particle swarm optimization), is adopted for visual tracking. Since the tracking process is a dynamic optimization problem which is simultaneously influenced by the object state and the time, we propose a sequential particle swarm optimization framework by incorporating the temporal continuity information into the traditional PSO algorithm. In addition, the parameters in PSO are changed adaptively according to the fitness values of particles and the predicted motion of the tracked object, leading to a favourable performance in tracking applications. Furthermore, we show theoretically that, in a Bayesian inference view, the sequential PSO framework is in essence a multilayer importance sampling based particle filter. Experimental results demonstrate that, compared with the state-of-the-art particle filter and its variation - the unscented particle filter, the proposed tracking algorithm is more robust and effective, especially when the object has an arbitrary motion or undergoes large appearance changes.
Taylor expansion based classifier adaptation: Application to person detection
Authors: Cha Zhang, Raffay Hamid, Zhengyou Zhang
Venue: CVPR
Year: 2008

Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In this paper, we present a general framework for classifier adaptation, which improves an existing generic classifier in the new test environment. Viewing classifier learning as a cost minimization problem, we perform classifier adaptation by combining the cost function on the old data-sets with the cost function on the data-set collected from the new environment. The former term is further approximated with its second order Taylor expansion to reduce the amount of information that needs to be saved for adaptation. Unlike traditional approaches that are often designed for a specific application or classifier, our scheme is applicable to various types of classifiers and user labels. We demonstrate this property on two popular classifiers (logistic regression and boosting), while using two types of user labels (direct labels and similarity labels). Extensive experiments conducted for the task of person detection in conference-room environments show that significant performance improvement can be achieved with our proposed method.
Practical camera auto-calibration based on object appearance and motion for traffic scene visual surveillance
Authors: Zhaoxiang Zhang 0001, Min Li 0022, Kaiqi Huang, Tieniu Tan
Venue: CVPR
Year: 2008

Camera calibration, as a fundamental issue in computer vision, is indispensable in many visual surveillance applications. Firstly, calibrated camera can help to deal with perspective distortion of object appearance on image plane. Secondly, calibrated camera makes it possible to recover metrics from images which are robust to scene or view an gle changes. In addition, with calibrated cameras, we can make use of prior information of 3D models to estimate 3D pose of objects and make object detection or tracking more robust to noise and occlusions. In this paper, we propose an automatic method to recover camera models from traffic scene surveillance videos. With only the camera height H measured, we can completely recover both intrinsic and extrinsic parameters of cameras based on appearance and motion of objects in videos. Experiments are conducted in different scenes and experimental results demonstrate the effectiveness and practicability of our approach, which can be adopted in many traffic scene surveillance applications.
Global data association for multi-object tracking using network flows
Authors: Li Zhang, Yuan Li 0022, Ramakant Nevatia
Venue: CVPR
Year: 2008

We propose a network flow based optimization method for data association needed for multiple object tracking. The maximum-a-posteriori (MAP) data association problem is mapped into a cost-flow network with a non-overlap constraint on trajectories. The optimal data association is found by a min-cost flow algorithm in the network. The network is augmented to include an Explicit Occlusion Model(EOM) to track with long-term inter-object occlusions. A solution to the EOM-based network is found by an iterative approach built upon the original algorithm. Initialization and termination of trajectories and potential false observations are modeled by the formulation intrinsically. The method is efficient and does not require hypotheses pruning. Performance is compared with previous results on two public pedestrian datasets to show its improvement.
L1 regularized projection pursuit for additive model learning
Authors: Xiao Zhang, Lin Liang, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2008

In this paper, we present a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized projection pursuit algorithm for additive model learning. Two new algorithms are developed for regression and classification respectively: sparse projection pursuit regression and sparse Jensen-Shannon Boosting. The introduced L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized projection pursuit encourages sparse solutions, thus our new algorithms are robust to overfitting and present better generalization ability especially in settings with many irrelevant input features and noisy data. To make the optimization with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization more efficient, we develop an ldquoinformative feature firstrdquo sequential optimization algorithm. Extensive experiments demonstrate the effectiveness of our proposed approach.
Semi-Supervised Discriminant Analysis using robust path-based similarity
Authors: Yu Zhang 0006, Dit-Yan Yeung
Venue: CVPR
Year: 2008

Linear discriminant analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality reduction technique in pattern recognition and machine learning. In real-world applications when labeled data are limited, LDA does not work well. Under many situations, however, it is easy to obtain unlabeled data in large quantities. In this paper, we propose a novel dimensionality reduction method, called semi-supervised discriminant analysis (SSDA), which can utilize both labeled and unlabeled data to perform dimensionality reduction in the semi-supervised setting. Our method uses a robust path-based similarity measure to capture the manifold structure of the data and then uses the obtained similarity to maximize the separability between different classes. A kernel extension of the proposed method for nonlinear dimensionality reduction in the semi-supervised setting is also presented. Experiments on face recognition demonstrate the effectiveness of the proposed method.
Cost-sensitive face recognition
Authors: Yin Zhang, Zhi-Hua Zhou
Venue: CVPR
Year: 2008

Traditional face recognition systems attempt to achieve a high recognition accuracy, which implicitly assumes that the losses of all misclassifications are the same. However, in many real-world tasks this assumption is not always reasonable. For example, it will be troublesome if a face-recognition-based door-locker misclassifies a family member as a stranger such that s/he were not allowed to enter the house; but it will be a much more serious disaster if a stranger were misclassified as a family member and allowed to enter the house. In this paper, we propose a framework which formulates the problem as a multi-class cost-sensitive learning task, and propose a theoretically sound method based on Bayes decision theory to solve this problem. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
Conditional density learning via regression with application to deformable shape segmentation
Authors: Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu, Leonard McMillan
Venue: CVPR
Year: 2008

Many vision problems can be cast as optimizing the conditional probability density function p(C\I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C\I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities of the model. We propose to learn a conditional probability density satisfying these desired properties for the given training data set. To do this, we formulate a novel regression problem that finds a function approximating the target density. Learning the regressor is challenging due to the high dimensionality of model parameters, C, and the complexity of relating the image and the model. Our approach makes two contributions. First, we take a multilevel refinement approach by learning a series of density functions, each of which guides the solution of optimization algorithms increasingly converging to the correct solution. Second, we propose a new data sampling algorithm that takes into account the gradient information of the target function. We have applied this learning approach to deformable shape segmentation and have achieved better accuracy than the previous methods.
Spectral methods for semi-supervised manifold learning
Authors: Zhenyue Zhang, Hongyuan Zha, Min Zhang
Venue: CVPR
Year: 2008

Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data points, we need to determine the parameter vectors for the rest of the data points. This problem is known as semi-supervised manifold learning, and in this paper we propose methods to handle this problem by solving certain eigenvalue-problems. Our proposed methods address two key issues in semi-supervised manifold learning: 1) fitting of the local affine geometric structures, and 2) preserving the global manifold structures embodied in the overlapping neighborhoods around each data points. We augment the alignment matrix of local tangent space alignment (LTSA) with the orthogonal projection based on the known parameter vectors, giving rise to the eigenvalue problem that characterizes the semi-supervised manifold learning problem. We also discuss the roles of different types of neighborhoods and their influence on the learning process. We illustrate the performance of the proposed methods using both synthetic data sets as well as data sets arising from applications in video annotations.