OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised learning of finite mixtures using entropy regularization and its application to image segmentation
Authors: Zhiwu Lu 0001, Yuxin Peng, Jianguo Xiao
Venue: CVPR
Year: 2008

When fitting finite mixtures to multivariate data, it is crucial to select the appropriate number of components. Under regularization theory, we aim to resolve this “unsupervised” learning problem via regularizing the likelihood by the full entropy of posterior probabilities for finite mixture fitting. Two deterministic annealing implementations are further proposed for this entropy regularized likelihood (ERL) learning. Through some asymptotic analysis of the deterministic annealing ERL (DAERL) learning, we find that the global minimization of the ERL function in an annealing way can lead to automatic model selection on finite mixtures and also make our DAERL algorithms less sensitive to initialization than the standard EM algorithm. The simulation experiments then demonstrate that our algorithms can provide some promising results just as our theoretic analysis. Moreover, our algorithms are evaluated in the application of unsupervised image segmentation and shown to outperform other state-of-the-art methods.
Enforcing non-positive weights for stable support vector tracking
Authors: Simon Lucey
Venue: CVPR
Year: 2008

In this paper we demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a weighted Euclidean norm. From this equivalence we empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically. We then propose a novel ldquononpositive support kernel machinerdquo (NSKM) to circumvent this limitation and allow the effective use of discriminative classification within the weighted LK framework. This approach ensures that the pseudo-Hessian realized within the weighted LK algorithm is positive semidefinite which allows for fast convergence and accurate alignment/tracking. A further benefit of our proposed method is that the NSKM solution results in a much sparser kernel machine than the canonical SVM leading to sizeable computational savings and much improved alignment performance.
Learning coupled conditional random field for image decomposition with application on object categorization
Authors: Xiaoxu Ma, W. Eric L. Grimson
Venue: CVPR
Year: 2008

This paper proposes a computational system of object categorization based on decomposition and adaptive fusion of visual information. A coupled Conditional Random Field is developed to model the interaction between low level cues of contour and texture, and to decompose contour and texture in natural images. The advantages of using coupled rather than single-layer Random Fields are demonstrated with model learning and evaluation. Multiple decomposed visual cues are adaptively combined for object categorization to fully leverage different discriminative cues for different classes. Experimental results show that the proposed computational model of “recognition-through-decomposition-and-fusion” achieves better performance than most of the state-of-the-art methods, especially when only a limited number of training samples are available.
Re-weighting Linear Discrimination Analysis under ranking loss
Authors: Yong Ma, Yoshihisa Ijiri, Shihong Lao, Masato Kawade
Venue: CVPR
Year: 2008

Linear discrimination analysis (LDA) is one of the most popular feature extraction and classifier design techniques. It maximizes the Fisher-ratio between between-class scatter matrix and within-class scatter matrix under a linear transformation, and the transformation is composed of the generalized eigenvectors of them. However, Fisher criterion itself can not decide the optimum norm of transformation vectors for classification. In this paper, we show that actually the norm of the transformation vectors has strong influence on classification performance, and we propose a novel method to estimate the optimum norm of LDA under the ranking loss, re-weighting LDA. On artificial data and real databases, the experiments demonstrate the proposed method can effectively improve the performance of LDA classifiers. And the algorithm can also be applied to other LDA variants such as non parametric discriminant analysis (NDA) to improve theirs performance further.
Symmetric multi-view stereo reconstruction from planar camera arrays
Authors: Matthieu Maitre, Yoshihisa Shinagawa, Minh N. Do
Venue: CVPR
Year: 2008

We present a novel stereo algorithm which performs surface reconstruction from planar camera arrays. It incorporates the merits of both generic camera arrays and rectified binocular setups, recovering large surfaces like the former and performing efficient computations like the latter. First, we introduce a rectification algorithm which gives freedom in the design of camera arrays and simplifies photometric and geometric computations. We then define a novel set of data-fusion functions over 4-neighborhoods of cameras, which treat all cameras symmetrically and enable standard binocular stereo algorithms to handle arrays with arbitrary number of cameras. In particular, we introduce a photometric fusion function which handles partial visibility and extracts depth information along both horizontal and vertical baselines. Finally, we show that layered depth images and sprites with depth can be efficiently extracted from the rectified 3D space. Experimental results on real images confirm the effectiveness of the proposed method, which reconstructs dense surfaces larger by 20% on Tsukuba.
Classification using intersection kernel support vector machines is efficient
Authors: Subhransu Maji, Alexander C. Berg, Jitendra Malik
Venue: CVPR
Year: 2008

Straightforward classification using kernelized SVMs requires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently. In particular we show that one can build histogram intersection kernel SVMs (IKSVMs) with runtime complexity of the classifier logarithmic in the number of support vectors as opposed to linear for the standard approach. We further show that by precomputing auxiliary tables we can construct an approximate classifier with constant runtime and space requirements, independent of the number of support vectors, with negligible loss in classification accuracy on various tasks. This approximation also applies to 1 - chi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and other kernels of similar form. We also introduce novel features based on a multi-level histograms of oriented edge energy and present experiments on various detection datasets. On the INRIA pedestrian dataset an approximate IKSVM classifier based on these features has the current best performance, with a miss rate 13% lower at 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> False Positive Per Window than the linear SVM detector of Dalal & Triggs. On the Daimler Chrysler pedestrian dataset IKSVM gives comparable accuracy to the best results (based on quadratic SVM), while being 15times faster. In these experiments our approximate IKSVM is up to 2000times faster than a standard implementation and requires 200times less memory. Finally we show that a 50times speedup is possible using approximate IKSVM based on spatial pyramid features on the Caltech 101 dataset with negligible loss of accuracy.
Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation
Authors: Nicolas Martel-Brisson, André Zaccarin
Venue: CVPR
Year: 2008

In background subtraction, cast shadows induce silhouette distortions and object fusions hindering performance of high level algorithms in scene monitoring. We introduce a nonparametric framework to model surface behavior when shadows are cast on them. Based on physical properties of light sources and surfaces, we identify a direction in RGB space on which background surface values under cast shadows are found. We then model the posterior distribution of lighting attenuation under cast shadows and foreground objects, which allows differentiation of foreground and cast shadow values with similar chromaticity. The algorithms are completely unsupervised and take advantage of scene activity to learn model parameters. Spatial gradient information is also used to reinforce the learning process. Contributions are two-fold. Firstly, with a better model describing cast shadows on surfaces, we achieve a higher success rate in segmenting moving cast shadows in complex scenes. Secondly, obtaining such models is a step toward a full scene parametrization where light source properties, surface reflectance models and scene 3D geometry are estimated for low-level segmentation.
Learning stick-figure models using nonparametric Bayesian priors over trees
Authors: Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis
Venue: CVPR
Year: 2008

We present a probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree in such a way that their parameter distributions are probabilistically centered around their parent node. This prior enables the inference procedures to learn multiple explanations for motion-capture data, each of which could be trees of different depth and path lengths. Thus, the algorithm can automatically determine a reasonable distribution over the number of sticks in a given dataset and their hierarchical relationships. We provide experimental results on several motion-capture datasets, demonstrating the modelpsilas ability to recover plausible stick-figure structure, and also the modelpsilas robust behavior when faced with occlusion.
Coherent image annotation by learning semantic distance
Authors: Tao Mei 0001, Yong Wang, Xian-Sheng Hua 0001, Shaogang Gong, Shipeng Li 0001
Venue: CVPR
Year: 2008

Conventional approaches to automatic image annotation usually suffer from two problems: (1) They cannot guarantee a good semantic coherence of the annotated words for each image, as they treat each word independently without considering the inherent semantic coherence among the words; (2) They heavily rely on visual similarity for judging semantic similarity. To address the above issues, we propose a novel approach to image annotation which simultaneously learns a semantic distance by capturing the prior annotation knowledge and propagates the annotation of an image as a whole entity. Specifically, a semantic distance function (SDF) is learned for each semantic cluster to measure the semantic similarity based on relative comparison relations of prior annotations. To annotate a new image, the training images in each cluster are ranked according to their SDF values with respect to this image and their corresponding annotations are then propagated to this image as a whole entity to ensure semantic coherence. We evaluate the innovative SDF-based approach on Corel images compared with Support Vector Machine-based approach. The experiments show that SDF-based approach outperforms in terms of semantic coherence, especially when each training image is associated with multiple words.
Edge descriptors for robust wide-baseline correspondence
Authors: Jason Meltzer, Stefano Soatto
Venue: CVPR
Year: 2008

This paper describes a method for finding wide-baseline correspondences between images at locations along gradient edges. We find edges in scale space using established methods and develop invariant descriptors for these edges based on orientation and scale histograms. Because edges are often found on occluding boundaries, we calculate and store two descriptors per edge, one on each side, for robustness to occlusions. We demonstrate the effectiveness of edge matching in the applications of wide-baseline correspondence, structure from motion from line segments, and object category recognition on the Caltech 101 dataset.
A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images
Authors: Eduardo Gerardo Mendizabal Ruiz, Mariano Rivera, Ioannis A. Kakadiaris
Venue: CVPR
Year: 2008

Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a probabilistic approach for the semi-automatic identification of the luminal border on IVUS images. Specifically, we parameterize the lumen contour using a mixture of Gaussian that is deformed by the minimization of a cost function formulated using a probabilistic approach. For the optimization of the cost function, we introduce a novel method that linearly combines the descent directions of the steepest descent and BFGS optimization methods within a trust region that improves convergence. Results of our proposed method on 20 MHz IVUS images are presented and discussed in order to demonstrate the effectiveness of our approach.
Superpixel lattices
Authors: Alastair Philip Moore, Simon Prince, Jonathan Warrell, Umar Mohammed, Graham Jones
Venue: CVPR
Year: 2008

Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Ideally, every pixel within each superpixel region will belong to the same real-world object. Existing algorithms generate superpixels that forfeit many useful properties of the regular topology of the original pixels: for example, the nth superpixel has no consistent position or relationship with its neighbors. We propose a novel algorithm that produces superpixels that are forced to conform to a grid (a regular superpixel lattice). Despite this added topological constraint, our algorithm is comparable in terms of speed and accuracy to alternative segmentation approaches. To demonstrate this, we use evaluation metrics based on (i) image reconstruction (ii) comparison to human-segmented images and (iii) stability of segmentation over subsequent frames of video sequences.
Finding trails
Authors: Scott Morris 0001, Kobus Barnard
Venue: CVPR
Year: 2008

We present a statistical learning approach for finding recreational trails in aerial images. While the problem of recognizing relatively straight and well defined roadways in digital images has been well studied in the literature, the more difficult problem of extracting trails has received no attention. However, trails and rough roads are less likely to be adequately mapped, and change more rapidly over time. Automated tools for finding trails will be useful to cartographers, recreational users and governments. In addition, the methods developed here are applicable to the more general problem of finding linear structure. Our approach combines local estimates for image pixel trail probabilities with the global constraint that such pixels must link together to form a path. For the local part, we present results using three classification techniques. To construct a global solution (a trail) from these probabilities, we propose a global cost function that includes both global probability and path length. We show that the addition of a length term significantly improves trail finding ability. However, computing the optimal trail becomes intractable as known dynamic programming methods do not apply. Thus we describe a new splitting heuristic based on Dijkstra's algorithm. We then further improve upon the results with a trail sampling scheme. We test our approach on 500 challenging images along the 2500 mile continental divide mountain bike trail, where assumptions prevalent in the road literature are violated.
Discriminative local binary patterns for human detection in personal album
Authors: Yadong Mu, Shuicheng Yan, Yi Liu, Thomas S. Huang, Bingfeng Zhou
Venue: CVPR
Year: 2008

In recent years, local pattern based object detection and recognition have attracted increasing interest in computer vision research community. However, to our best knowledge no previous work has focused on utilizing local patterns for the task of human detection. In this paper we develop a novel human detection system in personal albums based on LBP (local binary pattern) descriptor. Firstly we review the existing gradient based local features widely used in human detection, analyze their limitations and argue that LBP is more discriminative. Secondly, original LBP descriptor does not suit the human detecting problem well due to its high complexity and lack of semantic consistency, thus we propose two variants of LBP: Semantic-LBP and Fourier-LBP. Carefully designed experiments demonstrate the superiority of LBP over other traditional features for human detection. Especially we adopt a random ensemble algorithm for better comparison between different descriptors. All experiments are conducted on INRIA human database.
Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera
Authors: Vinay P. Namboodiri, Subhasis Chaudhuri
Venue: CVPR
Year: 2008

In this paper we investigate the challenging problem of recovering the depth layers in a scene from a single defocused observation. The problem is definitely solvable if there are multiple observations. In this paper we show that one can perceive the depth in the scene even from a single observation. We use the inhomogeneous reverse heat equation to obtain an estimate of the blur, thereby preserving the depth information characterized by the defocus. However, the reverse heat equation, due to its parabolic nature, is divergent. We stabilize the reverse heat equation by considering the gradient degeneration as an effective stopping criterion. The amount of (inverse) diffusion is actually a measure of relative depth. Because of ill-posedness we propose a graph-cuts based method for inferring the depth in the scene using the amount of diffusion as a data likelihood and a smoothness condition on the depth in the scene. The method is verified experimentally on a varied set of test cases.
View and scale invariant action recognition using multiview shape-flow models
Authors: Pradeep Natarajan, Ram Nevatia
Venue: CVPR
Year: 2008

Actions in real world applications typically take place in cluttered environments with large variations in the orientation and scale of the actor. We present an approach to simultaneously track and recognize known actions that is robust to such variations, starting from a person detection in the standing pose. In our approach we first render synthetic poses from multiple viewpoints using Mocap data for known actions and represent them in a conditional random field (CRF) whose observation potentials are computed using shape similarity and the transition potentials are computed using optical flow. We enhance these basic potentials with terms to represent spatial and temporal constraints and call our enhanced model the shape, flow, duration-conditional random field (SFD-CRF). We find the best sequence of actions using Viterbi search in the SFD-CRF. We demonstrate our approach on videos from multiple viewpoints and in the presence of background clutter.
Evaluation of constructable match cost measures for stereo correspondence using cluster ranking
Authors: Daniel Neilson, Yee-Hong Yang
Venue: CVPR
Year: 2008

Stereo correspondence research often involves the comparison of techniques to determine which are better under different circumstances. The methods of comparison employed often take the form of applying the techniques to a few stereo image pairs with the technique with the lowest error rate declared superior. However, the majority of these comparisons do not contain any discussion of statistical significance; making the declared superiority of a technique statistically unreliable. In this paper we present a new evaluation method called cluster ranking that yields a statistically significant comparison of the stereo techniques being compared. Cluster ranking leverages statistical inference techniques to first rank the performance of stereo techniques on a single stereo image pair and then combine the rankings from multiple stereo pairs into an over-all ranking; in both of these rankings, only stereo techniques that are statistically different are given different ranks. We demonstrate our framework with a comparison of constructable match cost measures (those that can be assembled from a base set of components) on a data set consisting of 30 synthetic stereo pairs, with varying amounts of noise, and 18 scenes from the 2005 and 2006 Middlebury data sets. Our analysis reveals match cost measures, and measure components, that are statistically superior to all other measures depending on amount of noise, illumination, or exposure time.
Epitomic location recognition
Authors: Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn
Venue: CVPR
Year: 2008

This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yield enhanced generalization with economical training. Experiments on both existing and new labelled image databases result in recognition accuracy superior to state of the art with real-time computational performance.
Shading models for illumination and reflectance invariant shape detectors
Authors: Peter Nillius, Josephine Sullivan, Antonis A. Argyros
Venue: CVPR
Year: 2008

Many objects have smooth surfaces of a fairly uniform color, thereby exhibiting shading patterns that reveal information about its shape, an important clue to the nature of the object. This papers explores extracting this information from images, by creating shape detectors based on shading.
Discriminative learning of visual words for 3D human pose estimation
Authors: Huazhong Ning, Wei Xu 0007, Yihong Gong, Thomas S. Huang
Venue: CVPR
Year: 2008

This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an Appearance and Position Context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.