OpenRaiser/Intern-Atlas huggingface dataset:
Clustering and dimensionality reduction on Riemannian manifolds
Authors: Alvina Goh, René Vidal
Venue: CVPR
Year: 2008

We propose a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of local nonlinear dimensionality reduction algorithms from Euclidean to Riemannian spaces. Such generalizations exploit geometric properties of the Riemannian space, particularly its Riemannian metric. Then, assuming that the data points from different groups are separated, we show that the null space of a matrix built from the local representation gives the segmentation of the data. Our method is computationally simple and performs automatic segmentation without requiring user initialization. We present results on 2-D motion segmentation and diffusion tensor imaging segmentation.
Fast approximate RandomWalker segmentation using eigenvector precomputation
Authors: Leo J. Grady, Ali Kemal Sinop
Venue: CVPR
Year: 2008

Interactive segmentation is often performed on images that have been stored on disk (e.g., a medical image server) for some time prior to user interaction. We propose to use this time to perform an offline precomputation of the segmentation prior to user interaction that significantly decreases the amount of user time necessary to produce a segmentation. Knowing how to effectively precompute the segmentation prior to user interaction is difficult, since a user may choose to guide the segmentation algorithm to segment any object (or multiple objects) in the image. Consequently, precomputation performed prior to user interaction must be performed without any knowledge of the user interaction. Specifically, we show that one may precompute several eigenvectors of the weighted Laplacian matrix of a graph and use this information to produce a linear-time approximation of the Random Walker segmentation algorithm, even without knowing where the foreground/background seeds will be placed. Finally, we also show that this procedure may be interpreted as a seeded (interactive) Normalized Cuts algorithm.
Matching vehicles under large pose transformations using approximate 3D models and piecewise MRF model
Authors: Yanlin Guo, Cen Rao, Supun Samarasekera, Janet Kim, Rakesh Kumar 0001, Harpreet S. Sawhney
Venue: CVPR
Year: 2008

We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion. The specific problem we solve is: given two views of an object, determine if the views are for the same or different object. Our domain of interest is vehicles, but the approach can be generalized to other man-made rigid objects. A key contribution of our approach is the use of approximate models with locally and globally constrained rendering to determine matching objects. We utilize a compact set of 3D models to provide geometry constraints and transfer appearance features for object matching across disparate viewpoints. The closest model from the set, together with its poses with respect to the data, is used to render an object both at pixel (local) level and region/part (global) level. Especially, symmetry and semantic part ownership are used to extrapolate appearance information. A piecewise Markov Random Field (MRF) model is employed to combine observations obtained from local pixel and global region level. Belief Propagation (BP) with reduced memory requirement is employed to solve the MRF model effectively. No training is required, and a realistic object image in a disparate viewpoint can be obtained from as few as just one image. Experimental results on vehicle data from multiple sensor platforms demonstrate the efficacy of our method.
Localization accuracy of region detectors
Authors: Andreas Haja, Bernd Jähne, Steffen Abraham
Venue: CVPR
Year: 2008

In this paper, a comparison of five state of the art region detectors is presented with regard to localization accuracy in position and region shape. Based on carefully estimated ground truth homographies, correspondences between frames are assigned using geometrical region overlap. Significant differences between detectors exist, depending on the type of images. Also, it is shown that localization accuracy linearly depends on region scale for some detectors, which may thus be used as a pre-selection criterion for the removal of error-prone regions. The presented results serve as a supplement to existing comparative studies, and can be used to facilitate the selection of an appropriate detector for a specific target application. When descriptor distance is used as assignment criterion instead of region overlap, a different set of correspondences results with lower accuracy. Set differences (and thus localization accuracy) are directly related to the density of regions in a local neighborhood. Based on the latter, a novel measure for the identification of error-prone regions - shape uniqueness - is introduced. In contrast to existing methods that are based on the descriptor distance of region correspondences, the new measure is pre-computed on each image individually. Thus, the complexity of the subsequent matching task can be significantly reduced.
Discovering class specific composite features through discriminative sampling with Swendsen-Wang Cut
Authors: Feng Han 0002, Ying Shan, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 2008

This paper proposes a novel approach to discover a set of class specific ldquocomposite featuresrdquo as the feature pool for the detection and classification of complex objects using AdaBoost. Each composite feature is constructed from the combination of multiple individual features. Unlike previous works that design features manually or with certain restrictions, the class specific features are selected from the space of all combinations of a set of individual features. To achieve this, we first establish an analogue between the problem of discriminative feature selection and generative image segmentation, and then draw discriminative samples from the combinatory space with a novel algorithm called discriminative generalized Swendsen-Wang cut. These samples form the initial pool of features, where AdaBoost is applied to learn a strong classifier combining the most discriminative composite features. We demonstrate the efficacy of our approach by comparing with existing detection algorithms for finding people in general pose.
Verifying global minima for L2 minimization problems
Authors: Richard I. Hartley, Yongduek Seo
Venue: CVPR
Year: 2008

We consider the least-squares (L2) triangulation problem and structure-and-motion with known rotatation, or known plane. Although optimal algorithms have been given for these algorithms under an L-infinity cost function, finding optimal least-squares (L2) solutions to these problems is difficult, since the cost functions are not convex, and in the worst case can have multiple minima. Iterative methods can usually be used to find a good solution, but this may be a local minimum. This paper provides a method for verifying whether a local-minimum solution is globally optimal, by providing a simple and rapid test involving the Hessian of the cost function. In tests of a data set involving 277,000 independent triangulation problems, it is shown that the test verifies the global optimality of an iterative solution in over 99.9% of the cases.
Intensity statistics-based HSI diffusion for color photo denoising
Authors: Lei He 0007, Chunming Li, Chenyang Xu 0001
Venue: CVPR
Year: 2008

This paper presents a new image denoising model for real color photo noise removal. Our model is implemented in the hue, saturation and intensity (HSI) space. The hue and saturation denoising are combined and implemented as a complex total variation (TV) diffusion. The intensity denoising is based on a diffusion flow to minimize a new energy functional, which is constructed with intensity component statistics. Besides the common gradient-based edge stopping functions for anisotropic diffusion, specifically for color photo denoising, we incorporate an intensity-based brightness adjusting term in the new energy, which corresponds to the noise disturbance with respect to photo intensity. In addition, we use the gradient vector flow (GVF) as the new diffusion directions for more accurate and robust denoising. Compared with previous diffusion flows only based on regular image gradients, this model provides more accurate image structure and intensity noise characterization for better denoising. Comprehensive quantitative and qualitative experiments on color photos demonstrate the improved performance of the proposed model when compared with 14 recognized approaches and 2 commercial software.
Latent topic random fields: Learning using a taxonomy of labels
Authors: Xuming He 0001, Richard S. Zemel
Venue: CVPR
Year: 2008

An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn from a semantic hierarchy, and can also readily cope with missing labels, and roughly-specified object boundaries. We introduce a new form of latent topic model, learning a novel context representation in the joint label-and-image space by capturing co-occurring patterns within and between image features and object labels. Given a topic, the model generates the input data, as well as a topic-dependent probabilistic classifier to predict labels for image regions. We present results on two real-world datasets, demonstrating significant improvements gained by including the coarsely labeled images.
Matching images under unstable segmentations
Authors: Varsha Hedau, Himanshu Arora, Narendra Ahuja
Venue: CVPR
Year: 2008

Region based features are getting popular due to their higher descriptive power relative to other features. However, real world images exhibit changes in image segments capturing the same scene part taken at different time, under different lighting conditions, from different viewpoints, etc. Segmentation algorithms reflect these changes, and thus segmentations exhibit poor repeatability. In this paper we address the problem of matching regions of similar objects under unstable segmentations. Merging and splitting of regions makes it difficult to find such correspondences using one-to-one matching algorithms. We present partial region matching as a solution to this problem. We assume that the high contrast, dominant contours of an object are fairly repeatable, and use them to compute partial matching cost (PMC) between regions. Region correspondences are obtained under region adjacency constraints encoded by region adjacency graph (RAG). We integrate PMC in a many-to-one label assignment framework for matching RAGs, and solve it using belief propagation. We show that our algorithm can match images of similar objects across unstable image segmentations. We also compare the performance of our algorithm with that of the standard one-to-one matching algorithm on three motion sequences. We conclude that our partial region matching approach is robust under segmentation irrepeatabilities.
Online learning of patch perspective rectification for efficient object detection
Authors: Stefan Hinterstoisser, Selim Benhimane, Nassir Navab, Pascal Fua, Vincent Lepetit
Venue: CVPR
Year: 2008

For a large class of applications, there is time to train the system. In this paper, we propose a learning-based approach to patch perspective rectification, and show that it is both faster and more reliable than state-of-the-art ad hoc affine region detection methods. Our method performs in three steps. First, a classifier provides for every keypoint not only its identity, but also a first estimate of its transformation. This estimate allows carrying out, in the second step, an accurate perspective rectification using linear predictors. We show that both the classifier and the linear predictors can be trained online, which makes the approach convenient. The last step is a fast verification - made possible by the accurate perspective rectification - of the patch identity and its sub-pixel precision position estimation. We test our approach on real-time 3D object detection and tracking applications. We show that we can use the estimated perspective rectifications to determine the object pose and as a result, we need much fewer correspondences to obtain a precise pose estimation.
Optical flow estimation using Fourier Mellin Transform
Authors: Huy Tho Ho, Roland Göcke
Venue: CVPR
Year: 2008

In this paper, we propose a novel method of computing the optical flow using the Fourier Mellin Transform (FMT). Each image in a sequence is divided into a regular grid of patches and the optical flow is estimated by calculating the phase correlation of each pair of co-sited patches using the FMT. By applying the FMT in calculating the phase correlation, we are able to estimate not only the pure translation, as limited in the case of the basic phase correlation techniques, but also the scale and rotation motion of image patches, i.e. full similarity transforms. Moreover, the motion parameters of each patch can be estimated to sub-pixel accuracy based on a recently proposed algorithm that uses a 2D esinc function in fitting the data from the phase correlation output. We also improve the estimation of the optical flow by presenting a method of smoothing the field by using a vector weighted average filter. Finally, experimental results, using publicly available data sets are presented, demonstrating the accuracy and improvements of our method over previous optical flow methods.
Semi-supervised SVM batch mode active learning for image retrieval
Authors: Steven C. H. Hoi, Rong Jin 0001, Jianke Zhu, Michael R. Lyu
Venue: CVPR
Year: 2008

Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to each other. In this paper, we propose a novel scheme that exploits both semi-supervised kernel learning and batch mode active learning for relevance feedback in CBIR. In particular, a kernel function is first learned from a mixture of labeled and unlabeled examples. The kernel will then be used to effectively identify the informative and diverse examples for active learning via a min-max framework. An empirical study with relevance feedback of CBIR showed that the proposed scheme is significantly more effective than other state-of-the-art approaches.
The scale of a texture and its application to segmentation
Authors: Byung-Woo Hong, Stefano Soatto, Kangyu Ni, Tony F. Chan
Venue: CVPR
Year: 2008

This paper examines the issue of scale in modeling texture for the purpose of segmentation. We propose a scale descriptor for texture and an energy minimization model to find the scale of a given texture at each location. For each pixel, we use the intensity distribution in a local patch around that pixel to determine the smallest size of the domain that can be used to generate neighboring patches. The energy functional we propose to minimize is comprised of three terms: The first is the dissimilarity measure using the Wasserstein distance or Kullback-Leibler divergence between neighboring patch distributions; the second maximizes the entropy of the local patch, and the third penalizes larger size at equal fidelity. Our experiments show the proposed scale model successfully captures the intrinsic scale of texture at each location. We also apply our scale descriptor for improving texture segmentation based on histogram matching [15].
Image selection for improved Multi-View Stereo
Authors: Alexander Hornung, Boyi Zeng, Leif Kobbelt
Venue: CVPR
Year: 2008

The Middlebury multi-view stereo evaluation clearly shows that the quality and speed of most multi-view stereo algorithms depends significantly on the number and selection of input images. In general, not all input images contribute equally to the quality of the output model, since several images may often contain similar and hence overly redundant visual information. This leads to unnecessarily increased processing times. On the other hand, a certain degree of redundancy can help to improve the reconstruction in more ldquodifficultrdquo regions of a model. In this paper we propose an image selection scheme for multi-view stereo which results in improved reconstruction quality compared to uniformly distributed views. Our method is tuned towards the typical requirements of current multi-view stereo algorithms, and is based on the idea of incrementally selecting images so that the overall coverage of a simultaneously generated proxy is guaranteed without adding too much redundant information. Critical regions such as cavities are detected by an estimate of the local photo-consistency and are improved by adding additional views. Our method is highly efficient, since most computations can be out-sourced to the GPU. We evaluate our method with four different methods participating in the Middlebury benchmark and show that in each case reconstructions based on our selected images yield an improved output quality while at the same time reducing the processing time considerably.
Multiple-instance ranking: Learning to rank images for image retrieval
Authors: Yang Hu 0006, Mingjing Li, Nenghai Yu
Venue: CVPR
Year: 2008

We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images into relevant and irrelevant images and learn a binary classifier, we learn the ranking model from image pairs with preference relations. In addition to the relevance of images, we are further interested in what portion of the image is of interest to the user. Therefore, we consider images represented by sets of regions and propose multiple-instance rank learning based on the max margin framework. Three different schemes are designed to encode the multiple-instance assumption. We evaluate the performance of the multiple-instance ranking algorithms on real-word images collected from Flickr - a popular photo sharing service. The experimental results show that the proposed algorithms are capable of learning effective ranking models for image retrieval.
A framework for reducing ink-bleed in old documents
Authors: Yi Huang 0001, Michael S. Brown, Dong Xu
Venue: CVPR
Year: 2008

We describe a novel application framework to reduce the effects of ink-bleed in old documents. This task is treated as a classification problem where training-data is used to compute per-pixel likelihoods for use in a dual-layer Markov Random Field (MRF) that simultaneously labels image pixels of the front and back of a document as either foreground, background, or ink-bleed, while maintaining the integrity of foreground strokes. Our approach obtains better results than previous work without the need for assumptions about ink-bleed intensities or extensive parameter tuning. Our overall framework is detailed, including front and back image alignment, training-data collection, and the MRF formulation with associated likelihoods and intra- and interlayer cost computations.
Enhanced biologically inspired model
Authors: Yongzhen Huang, Kaiqi Huang, Liangsheng Wang, Dacheng Tao, Tieniu Tan, Xuelong Li 0001
Venue: CVPR
Year: 2008

It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the following three problems: a very heavy computational cost due to dense input, a disputable pooling operation in modeling relations of the visual cortex, and blind feature selection in a feed-forward framework. To solve these problems, we develop an enhanced BIM (EBIM), which removes uninformative input by imposing sparsity constraints, utilizes a novel local weighted pooling operation with stronger physiological motivations, and applies a feedback procedure that selects effective features for combination. Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM. We also apply EBIM to the MIT-CBCL street scene database to show it achieves comparable performance in comparison with the current best performance. Moreover, the new system can process images with resolution 128 times 128 at a rate of 50 frames per second and enhances the speed 20 times at least in comparison with BIM in common applications.
Demosaicking recognition with applications in digital photo authentication based on a quadratic pixel correlation model
Authors: Yizhen Huang, Yangjing Long
Venue: CVPR
Year: 2008

Most digital still color cameras use a single electronic sensor (CCD or CMOS) overlaid with a color filter array. At each pixel location only one color sample is taken, and the other colors must be interpolated using neighboring samples. This color plane interpolation is known as demosaicking, which is one of the important tasks in a digital camera pipeline. Demosaicked images possess spatially periodic inter-pixel correlation. In this paper, such correlation is expressed in a quadratic form, and Principal Component Analysis is applied to filter out intrinsic scene correlation. A decision mechanism using BP neural networks and a majority-voting scheme is designed to recognize demosaicking correlation and authenticate digital photos. Experiments show that, the proposed method can accurately classify images by demosaicking algorithms or source cameras, and it is effective to detect rendering forgeries. The sensitivity and robustness of the method are also verified. This algorithm-independent approach is especially useful when demosaicking algorithm is only available in form of binary code or integrated circuit without technical detail.
Silhouette-based camera calibration from sparse views under circular motion
Authors: Po-Hao Huang, Shang-Hong Lai
Venue: CVPR
Year: 2008

In this paper, we propose a new approach to camera calibration from silhouettes under circular motion with minimal data. We exploit the mirror symmetry property and derive a common homography that relates silhouettes with epipoles under circular motion. With the epipoles determined, the homography can be computed from the frontier points induced by epipolar tangencies. On the other hand, given the homography, the epipoles can be located directly from the bi-tangent lines of silhouettes. With the homography recovered, the image invariants under circular motion and camera parameters can be determined. If the epipoles are not available, camera parameters can be determined by a low-dimensional search of the optimal homography in a bounded region. In the degenerate case, when the camera optical axes intersect at one point, we derive a closed-form solution for the focal length to solve the problem. By using the proposed algorithm, we can achieve camera calibration simply from silhouettes of three images captured under circular motion. Experimental results on synthetic and real images are presented to show its performance.