OpenRaiser/Intern-Atlas huggingface dataset:
Beyond the graphs: Semi-parametric semi-supervised discriminant analysis
Authors: Fei Wang 0001, Xin Wang 0013, Tao Li 0001
Venue: CVPR
Year: 2009

Linear discriminant analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data, which makes the estimated projection direction inaccurate. To address the above limitations, in this paper, we propose a novel semi-supervised discriminant analysis approach. Unlike traditional graph based methods, our algorithm incorporates the geometric information revealed by both labeled and unlabeled data points in a semi-parametric way. Specifically, the final projections of the data points will contain two parts: a discriminant part learned by traditional LDA (or KDA) on the labeled points and a geometrical part learned by kernel PCA on the whole data set. Therefore we call our algorithm semi-parametric semi-supervised discriminant analysis (SSDA). Experimental results on face recognition and image retrieval tasks are presented to show the effectiveness of our method.
Dimension-free affine shape matching through subspace invariance
Authors: Zhaozhong Wang, Han Xiao
Venue: CVPR
Year: 2009

This paper proposes an affine invariant matching algorithm for shape correspondence problems in arbitrary dimensions. Formulating shapes by configuration matrices of landmarks, and using the fact that subspaces (e.g. range spaces) of these matrices are invariant to affine transformations, the shape correspondence is modelled as a permutation relation between orthogonal projection matrices of the subspaces. Then the matching result is solved by an efficient factorization procedure for rank-deficient matrices. The algorithm is compact, fast, and independent of dimensions. Experimental results for 1D, 2D and 3D matchings of synthetic and real data are provided, which demonstrate potential applications of the algorithm to shape analysis, and to other related problems like wide baseline stereo matching and range data registration.
Resolution-Invariant Image Representation and its applications
Authors: Jinjun Wang, Shenghuo Zhu, Yihong Gong
Venue: CVPR
Year: 2009

We present a resolution-invariant image representation (RIIR) framework in this paper. The RIIR framework includes the methods of building a set of multi-resolution bases from training images, estimating the optimal sparse resolution-invariant representation of any image, and reconstructing the missing patches of any resolution level. As the proposed RIIR framework has many potential resolution enhancement applications, we discuss three novel image magnification applications in this paper. In the first application, we apply the RIIR framework to perform Multi-Scale Image Magnification where we also introduced a training strategy to built a compact RIIR set. In the second application, the RIIR framework is extended to conduct Continuous Image Scaling where a new base at any resolution level can be generated using existing RIIR set on the fly. In the third application, we further apply the RIIR framework onto Content-Base Automatic Zooming applications. The experimental results show that in all these applications, our RIIR based method outperforms existing methods in various aspects.
Noninvasive volumetric imaging of cardiac electrophysiology
Authors: Linwei Wang, Heye Zhang, Ken C. L. Wong, Huafeng Liu 0003, Pengcheng Shi
Venue: CVPR
Year: 2009

Volumetric details of cardiac electrophysiology, such as transmembrane potential dynamics and tissue excitability of the myocardium, are of fundamental importance for understanding normal and pathological cardiac mechanisms, and for aiding the diagnosis and treatment of cardiac arrhythmia. Noninvasive observations, however, are made on body surface as an integration-projection of the volumetric phenomena inside patient's heart. We present a physiological-model-constrained statistical framework where prior knowledge of general myocardial electrical activity is used to guide the reconstruction of patient-specific volumetric cardiac electrophysiological details from body surface potential data. Sequential data assimilation with proper computational reduction is developed to estimate transmembrane potential and myocardial excitability inside the heart, which are then utilized to depict arrhythmogenic substrates. Effectiveness and validity of the framework is demonstrated through its application to evaluate the location and extent of myocardial infract using real patient data.
Boosted multi-task learning for face verification with applications to web image and video search
Authors: Xiaogang Wang, Cha Zhang, Zhengyou Zhang
Venue: CVPR
Year: 2009

Face verification has many potential applications including filtering and ranking image/video search results on celebrities. Since these images/videos are taken under uncontrolled environments, the problem is very challenging due to dramatic lighting and pose variations, low resolutions, compression artifacts, etc. In addition, the available number of training images for each celebrity may be limited, hence learning individual classifiers for each person may cause overfitting. In this paper, we propose two ideas to meet the above challenges. First, we propose to use individual bins, instead of whole histograms, of Local Binary Patterns (LBP) as features for learning, which yields significant performance improvements and computation reduction in our experiments. Second, we present a novel Multi-Task Learning (MTL) framework, called Boosted MTL, for face verification with limited training data. It jointly learns classifiers for multiple people by sharing a few boosting classifiers in order to avoid overfitting. The effectiveness of Boosted MTL and LBP bin features is verified with a large number of celebrity images/videos from the web.
Epitomized priors for multi-labeling problems
Authors: Jonathan Warrell, Simon J. D. Prince, Alastair Philip Moore
Venue: CVPR
Year: 2009

Image parsing remains difficult due to the need to combine local and contextual information when labeling a scene. We approach this problem by using the epitome as a prior over label configurations. Several properties make it suited to this task. First, it allows a condensed patch-based representation. Second, efficient E-M based learning and inference algorithms can be used. Third, non-stationarity is easily incorporated. We consider three existing priors, and show how each can be extended using the epitome. The simplest prior assumes patches of labels are drawn independently from either a mixture model or an epitome. Next we investigate a `conditional epitome' model, which substitutes an epitome for a conditional mixture model. Finally, we develop an `epitome tree' model, which combines the epitome with a tree structured belief network prior. Each model is combined with a per-pixel classifier to perform segmentation. In each case, the epitomized form of the prior provides superior segmentation performance, with the epitome tree performing best overall. We also apply the same models to denoising binary images, with similar results.
The geometry of 2D image signals
Authors: Lennart Wietzke, Gerald Sommer, Oliver Fleischmann
Venue: CVPR
Year: 2009

This paper covers a fundamental problem of local phase based signal processing: the isotropic generalization of the classical 1D analytic signal to two dimensions. The well known analytic signal enables the analysis of local phase and amplitude information of 1D signals. Local phase, amplitude and additional orientation information can be extracted by the 2D monogenic signal with the restriction to the subclass of intrinsically one dimensional signals. In case of 2D image signals the monogenic signal enables the rotationally invariant analysis of lines and edges. In this work we present the 2D analytic signal as a novel generalization of both the analytic signal and the 2D monogenic signal. In case of 2D image signals the 2D analytic signal enables the isotropic analysis of lines, edges, corners and junctions in one unified framework. Furthermore, we show that 2D signals exist per se in a 3D projective subspace of the homogeneous conformal space which delivers a descriptive geometric interpretation of signals providing new insights on the relation of geometry and 2D signals.
A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis
Authors: Dijia Wu, Jinbo Bi, Kim L. Boyer
Venue: CVPR
Year: 2009

The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characteristics: extremely unbalanced data between negative and positive classes; stringent real-time requirement of online execution; multiple positive candidates generated for the same malignant structure that are highly correlated and spatially close to each other. To address all these problems, we propose a novel learning formulation to combine cascade classification and multiple instance learning (MIL) in a unified min-max framework, leading to a joint optimization problem which can be converted to a tractable quadratically constrained quadratic program and efficiently solved by block-coordinate optimization algorithms. We apply the proposed approach to the CAD problems of detecting pulmonary embolism and colon cancer from computed tomography images. Experimental results show that our approach significantly reduces the computational cost while yielding comparable detection accuracy to the current state-of-the-art MIL or cascaded classifiers. Although not specifically designed for balanced MIL problems, the proposed method achieves superior performance on balanced MIL benchmark data such as MUSK and image data sets.
Contextual flow
Authors: Ying Wu 0001, Jialue Fan
Venue: CVPR
Year: 2009

Matching based on local brightness is quite limited, because small changes on local appearance invalidate the constancy in brightness. The root of this limitation is its treatment regardless of the information from the spatial contexts. This papers leaps from brightness constancy to context constancy, and thus from optical flow to contextual flow. It presents a new approach that incorporates contexts to constrain motion estimation for target tracking. In this approach, one individual spatial context of a given pixel is represented by the posterior density of the associated feature class in its contextual domain. Each individual context gives a linear contextual flow constraint to the motion, so that the motion can be estimated in an over-determined contextual system. Based on this contextual flow model, this paper presents a new and powerful target tracking method that integrates the processes of salient contextual point selection, robust contextual matching, and dynamic context selection. Extensive experiment results show the effectiveness of the proposed approach.
Bundling features for large scale partial-duplicate web image search
Authors: Zhong Wu, Qifa Ke, Michael Isard, Jian Sun 0001
Venue: CVPR
Year: 2009

In state-of-the-art image retrieval systems, an image is represented by a bag of visual words obtained by quantizing high-dimensional local image descriptors, and scalable schemes inspired by text retrieval are then applied for large scale image indexing and retrieval. Bag-of-words representations, however: 1) reduce the discriminative power of image features due to feature quantization; and 2) ignore geometric relationships among visual words. Exploiting such geometric constraints, by estimating a 2D affine transformation between a query image and each candidate image, has been shown to greatly improve retrieval precision but at high computational cost. In this paper we present a novel scheme where image features are bundled into local groups. Each group of bundled features becomes much more discriminative than a single feature, and within each group simple and robust geometric constraints can be efficiently enforced. Experiments in Web image search, with a database of more than one million images, show that our scheme achieves a 49% improvement in average precision over the baseline bag-of-words approach. Retrieval performance is comparable to existing full geometric verification approaches while being much less computationally expensive. When combined with full geometric verification we achieve a 77% precision improvement over the baseline bag-of-words approach, and a 24% improvement over full geometric verification alone.
A revisit of Generative Model for Automatic Image Annotation using Markov Random Fields
Authors: Yu Xiang, Xiangdong Zhou, Tat-Seng Chua, Chong-Wah Ngo
Venue: CVPR
Year: 2009

Much research effort on Automatic Image Annotation (AIA) has been focused on Generative Model, due to its well formed theory and competitive performance as compared with many well designed and sophisticated methods. However, when considering semantic context for annotation, the model suffers from the weak learning ability. This is mainly due to the lack of parameter setting and appropriate learning strategy for characterizing the semantic context in the traditional generative model. In this paper, we present a new approach based on Multiple Markov Random Fields (MRF) for semantic context modeling and learning. Differing from previous MRF related AIA approach, we explore the optimal parameter estimation and model inference systematically to leverage the learning power of traditional generative model. Specifically, we propose new potential function for site modeling based on generative model and build local graphs for each annotation keyword. The parameter estimation and model inference is performed in local optimal sense. We conduct experiments on commonly used benchmarks. On Corel 5000 images [3], we achieved 0.36 and 0.31 in recall and precision respectively on 263 keywords. This is a very significant improvement over the best reported result of the current state-of-the-art approaches.
Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses
Authors: Junliang Xing, Haizhou Ai, Shihong Lao
Venue: CVPR
Year: 2009

This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method.
A distribution-based approach to tracking points in velocity vector fields
Authors: Liefei Xu, H. Quynh Dinh, Eugene Zhang, Zhongzang Lin, Robert S. Laramee
Venue: CVPR
Year: 2009

We address the problem of tracking points in dense vector fields. Such vector fields may come from computational fluid dynamics simulations, environmental monitoring sensors, or dense point tracking of video data. To track points in vector fields, we capture the distribution of higher-order properties (e.g., properties derived from the gradient of the velocity vector field) in a novel local descriptor called a vector spin-image. Our distribution-based approach has a number of advantages over methods that use topology analysis to track points in vector fields. The local distributions are robust to noise, adaptable to changes in the feature, and can be used to extrapolate the location of features after they have disappeared. We describe the vector spin-image data structure, the higher-order properties we record to track vector field points, and show results of tracking points in the simulated flow through a diesel engine cylinder.
Real-time O(1) bilateral filtering
Authors: Qingxiong Yang, Kar-Han Tan, Narendra Ahuja
Venue: CVPR
Year: 2009

We propose a new bilateral filtering algorithm with computational complexity invariant to filter kernel size, so-called O(1) or constant time in the literature. By showing that a bilateral filter can be decomposed into a number of constant time spatial filters, our method yields a new class of constant time bilateral filters that can have arbitrary spatial and arbitrary range kernels. In contrast, the current available constant time algorithm requires the use of specific spatial or specific range kernels. Also, our algorithm lends itself to a parallel implementation leading to the first real-time O(1) algorithm that we know of. Meanwhile, our algorithm yields higher quality results since we are effectively quantizing the range function instead of quantizing both the range function and the input image. Empirical experiments show that our algorithm not only gives higher PSNR, but is about 10× faster than the state-of-the-art. It also has a small memory footprint, needed only 2% of the memory required by the state-of-the-art for obtaining the same quality as exact using 8-bit images. We also show that our algorithm can be easily extended for O(1) median filtering. Our bilateral filtering algorithm was tested in a number of applications, including HD video conferencing, video abstraction, highlight removal, and multi-focus imaging.
Linear spatial pyramid matching using sparse coding for image classification
Authors: Jianchao Yang, Kai Yu 0001, Yihong Gong, Thomas S. Huang
Venue: CVPR
Year: 2009

Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ∼ n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handlemore than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.
Nonrigid registration combining global and local statistics
Authors: Zhao Yi, Stefano Soatto
Venue: CVPR
Year: 2009

In this paper we exploit normalized mutual information for the nonrigid registration of multimodal images. Rather than assuming that image statistics are spatially stationary, as often done in traditional information-theoretic methods, we take into account the spatial variability through a weighted combination of global normalized mutual information and local matching statistics. Spatial relationships are incorporated into the registration criterion by adoptively adjusting the weight according to the strength of local cues. With a continuous representation of images and Parzen window estimators, we have developed closed-form expressions of the first-order variation with respect to any general, nonparametric, infinite-dimensional deformation of the image domain. To characterize the performance of the proposed approach, synthetic phantoms, simulated MRIs, and clinical data are used in a validation study. The results suggest that the augmented normalized mutual information provides substantial improvements in terms of registration accuracy and robustness.
Shape constrained figure-ground segmentation and tracking
Authors: Zhaozheng Yin, Robert T. Collins
Venue: CVPR
Year: 2009

Global shape information is an effective top-down complement to bottom-up figure-ground segmentation as well as a useful constraint to avoid drift during adaptive tracking. We propose a novel method to embed global shape information into local graph links in a Conditional Random Field (CRF) framework. Given object shapes from several key frames, we automatically collect a shape dataset on-the-fly and perform statistical analysis to build a collection of deformable shape templates representing global object shape. In new frames, simulated annealing and local voting align the deformable template with the image to yield a global shape probability map. The global shape probability is combined with a region-based probability of object boundary map and the pixel-level intensity gradient to determine each link cost in the graph. The CRF energy is minimized by min-cut, followed by Random Walk on the uncertain boundary region to get a soft segmentation result. Experiments on both medical and natural images with deformable object shapes are demonstrated.
Angular embedding: From jarring intensity differences to perceived luminance
Authors: Stella X. Yu
Venue: CVPR
Year: 2009

Our goal is to turn an intensity image into its perceived luminance without parsing it into depths, surfaces, or scene illuminations. We start with jarring intensity differences at two scales mixed according to edges, identified by a pixel-centric edge detector. We propose angular embedding as a more robust, efficient, and versatile alternative to LS, LLE, and NCUTS for obtaining a global brightness ordering from local differences. Our model explains a variety of brightness illusions with a single algorithm. Brightness of a pixel can be understood locally as its intensity deviating in the gradient direction and globally as finding its rank relative to others, particularly the lightest and darkest ones.
Motion pattern interpretation and detection for tracking moving vehicles in airborne video
Authors: Qian Yu, Gérard G. Medioni
Venue: CVPR
Year: 2009

Detection and tracking of moving vehicles in airborne videos is a challenging problem. Many approaches have been proposed to improve motion segmentation on frame-by-frame and pixel-by-pixel bases, however, little attention has been paid to analyze the long-term motion pattern, which is a distinctive property for moving vehicles in airborne videos. In this paper, we provide a straightforward geometric interpretation of a general motion pattern in 4D space (x, y, v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> ). We propose to use the tensor voting computational framework to detect and segment such motion patterns in 4D space. Specifically, in airborne videos, we analyze the essential difference in motion patterns caused by parallax and independent moving objects, which leads to a practical method for segmenting motion patterns (flows) created by moving vehicles in stabilized airborne videos. The flows are used in turn to facilitate detection and tracking of each individual object in the flow. Conceptually, this approach is similar to “track-before-detect” techniques, which involves temporal information in the process as early as possible. As shown in the experiments, many difficult cases in airborne videos, such as parallax, noisy background modeling and long term occlusions, can be addressed by our approach.