OpenRaiser/Intern-Atlas huggingface dataset:
Classification via semi-Riemannian spaces
Authors: Deli Zhao, Zhouchen Lin, Xiaoou Tang
Venue: CVPR
Year: 2008

In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptualized as a semi-Riemannian manifold which is considered as a submanifold embedded in an ambient semi-Riemannian space. The class structures of original samples can be characterized and deformed by local metrics of the semi-Riemannian space. Semi-Riemannian metrics are uniquely determined by the smoothing of discrete functions and the nullity of the semi-Riemannian space. Based on the geometrization of class structures, optimizing class structures in the feature space is equivalent to maximizing the quadratic quantities of metric tensors in the semi-Riemannian space. Thus supervised discriminant subspace learning reduces to unsupervised semi-Riemannian manifold learning. Based on the proposed framework, a novel algorithm, dubbed as Semi-Riemannian Discriminant Analysis (SRDA), is presented for subspace-based classification. The performance of SRDA is tested on face recognition (singular case) and handwritten capital letter classification (nonsingular case) against existing algorithms. The experimental results show that SRDA works well on recognition and classification, implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning.
Visual Synset: Towards a higher-level visual representation
Authors: Yantao Zheng, Ming Zhao 0003, Shi-Yong Neo, Tat-Seng Chua, Qi Tian 0002
Venue: CVPR
Year: 2008

We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.
Robust 3D face recognition in uncontrolled environments
Authors: Cheng Zhong, Zhenan Sun, Tieniu Tan, Zhaofeng He
Venue: CVPR
Year: 2008

Most current 3D face recognition algorithms are designed based on the data collected in controlled situations, which leads to the un-guaranteed performance in practical systems. In this paper, we propose a Robust Local Log-Gabor Histograms (RLLGH) method to handle the uncontrolled problems encountered in 3D face recognition. In this challenging topic, large expressions and data noises are two main obstacles. To overcome the large expressions, we choose Log-Gabor features (LGF) to extract the distinctive and robust information embedded in 3D faces, which will be represented as 3D Log-Gabor faces. Data noises are summarized as distorted meshes, hair occlusions and misalignments. To overcome these problems, we introduce a robust local histogram (RLH) strategy, which takes advantage of the robustness of the accurate local statistical information. The combination of LGF and RLH leads to RLLGH. The novelties of this paper come from 1) Our work aims at studying 3D face recognition performance in uncontrolled environments; 2) We find that embedding LGF into the LVC framework leads to robustness in handling large expression variations; 3) The RLH strategy gives a promising way to solve the data noises problem. Our experiments are based on the large expression subset in FRGC2.0 3D face database and the expression subset in CASIA 3D face database. Experimental results show the efficiency, robustness and generalization of our proposed method.
A quasi-random sampling approach to image retrieval
Authors: Jun Zhou 0001, Antonio Robles-Kelly
Venue: CVPR
Year: 2008

In this paper, we present a novel approach to contents-based image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are related to a query image provided by the user. Departing from random sampling theory, we make use of the EM algorithm so as to organize the images in the database into compact clusters that can then be used for stratified random sampling. For the purposes of retrieval, we use the similarity between the query and the clustered images to govern the sampling process within clusters. In this way, the sampling can be viewed as a stratified sampling one which is random at the cluster level and takes into account the intra-cluster structure of the dataset. This approach leads to a measure of statistical confidence that relates to the theoretical hard-limit of the retrieval performance. We show results on the Oxford Flowers dataset.
Improved building detection by Gaussian processes classification via feature space rescale and spectral kernel selection
Authors: Hang Zhou 0005, David Suter
Venue: CVPR
Year: 2008

We use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as well as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data we compare the frequency domain features of each dimension to estimate a rescaling (towards making the data isotropic). Also, the spectrum of the training data is compared with several candidate kernel spectrums. From this comparison the best matching kernel is chosen. In these ways, the training data matches better the GP classification kernel function (and hence the underlying assumed correlation characteristics), resulting in a better GP classification result. Test results on both non image and image data show the efficiency and effectiveness of our approach.
Pair-activity classification by bi-trajectories analysis
Authors: Yue Zhou, Shuicheng Yan, Thomas S. Huang
Venue: CVPR
Year: 2008

In this paper, we address the pair-activity classification problem, which explores the relationship between two active objects based on their motion information. Our contributions are three-fold. First, we design a set of features, e.g., causality ratio and feedback ratio based on the Granger Causality Test (GCT), for describing the pair-activities encoded as trajectory pairs. These features along with conventional velocity and position features are essentially of multi-modalities, and may be greatly different in scale and importance. To make full use of them, we then present a novel feature normalization procedure to learn the coefficients for weighting these features by maximizing the discriminating power measured by weighted correlation. Finally, we collected a pair-activity database of five categories, each of which consists of about 170 instances. The extensive experiments on this database validate the effectiveness of the designed features for pair-activity representation, and also demonstrate that the proposed feature normalization procedure greatly boosts the pair-activity classification accuracy.
Real-time global localization with a pre-built visual landmark database
Authors: Zhiwei Zhu, Taragay Oskiper, Supun Samarasekera, Rakesh Kumar 0001, Harpreet S. Sawhney
Venue: CVPR
Year: 2008

In this paper, we study how to build a vision-based system for global localization with accuracies within 10cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate global localization. Although the accuracy of traditional short-term motion based visual odometry systems has improved significantly in recent years, these systems alone cannot solve the drift problem over large areas. Landmark based localization combined with visual odometry is a viable solution to the large scale localization problem. However, a systematic study of the specification and use of such a landmark database has not been undertaken. We propose techniques to build and optimize a landmark database systematically and efficiently using visual odometry. First, topology inference is utilized to find overlapping images in the database. Second, bundle adjustment is used to refine the accuracy of each 3D landmark. Finally, the database is optimized to balance the size of the database with achievable accuracy. Once the landmark database is obtained, a new real-time global localization methodology that works both indoors and outdoors is proposed. We present results of our study on both synthetic and real datasets that help us determine critical design parameters for the landmark database and the achievable accuracies of our proposed system.
Fusion of time-of-flight depth and stereo for high accuracy depth maps
Authors: Jiejie Zhu, Liang Wang 0002, Ruigang Yang, James Davis 0001
Venue: CVPR
Year: 2008

Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturerpsilas calibration. We show that these techniques lead to improved accuracy and robustness.
Computing minimal deformations: application to construction of statistical shape models
Authors: Darko Zikic, Michael Sass Hansen, Ben Glocker, Ali Khamene, Rasmus Larsen 0001, Nassir Navab
Venue: CVPR
Year: 2008

Nonlinear registration is mostly performed after initialization by a global, linear transformation (in this work, we focus on similarity transformations), computed by a linear registration method. For the further processing of the results, it is mostly assumed that this preregistration step completely removes the respective linear transformation. However, we show that in deformable settings, this is not the case. As a consequence, a significant linear component is still existent in the deformation computed by the nonlinear registration algorithm. For construction of statistical shape models (SSM) from deformations, this is an unwanted property: SSMs should not contain similarity transformations, since these do not capture information about shape. We propose a method which performs an a posteriori extraction of a similarity transformation from a given nonlinear deformation field, and we use the processed fields as input for SSM construction. For computation of minimal displacements, a closed-form solution minimizing the squared Euclidean norm of the displacement field subject to similarity parameters is used. Experiments on real inter-subject data and on a synthetic example show that the theoretically justified removal of the similarity component by the proposed method has a large influence on the shape model and significantly improves the results.
Learning photometric invariance from diversified color model ensembles
Authors: José M. Álvarez 0004, Theo Gevers, Antonio M. López 0001
Venue: CVPR
Year: 2009

Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.
On compositional Image Alignment, with an application to Active Appearance Models
Authors: Brian Amberg, Andrew Blake 0001, Thomas Vetter
Venue: CVPR
Year: 2009

Efficient and accurate fitting of active appearance models (AAM) is a key requirement for many applications. The most efficient fitting algorithm today is inverse compositional image alignment (ICIA). While ICIA is extremely fast, it is also known to have a small convergence radius. Convergence is especially bad when training and testing images differ strongly, as in multi-person AAMs. We describe “forward” compositional image alignment in a consistent framework which also incorporates methods previously termed “inverse” compositional, and use it to develop two novel fitting methods. The first method, compositional gradient descent (CoDe), is approximately four times slower than ICIA, while having a convergence radius which is even larger than that achievable by direct quasi-Newton descent. An intermediate convergence range with the same speed as ICIA is achieved by LinCoDe, the second new method. The success rate of the novel methods is 10 to 20 times higher than that of the original ICIA method.
Efficient algorithms for subwindow search in object detection and localization
Authors: Senjian An, Patrick Peursum, Wanquan Liu, Svetha Venkatesh
Venue: CVPR
Year: 2009

Recently, a simple yet powerful branch-and-bound method called Efficient Subwindow Search (ESS) was developed to speed up sliding window search in object detection. A major drawback of ESS is that its computational complexity varies widely from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) for n × n matrices. Our experimental experience shows that the ESS's performance is highly related to the optimal confidence levels which indicate the probability of the object's presence. In particular, when the object is not in the image, the optimal subwindow scores low and ESS may take a large amount of iterations to converge to the optimal solution and so perform very slow. Addressing this problem, we present two significantly faster methods based on the linear-time Kadane's Algorithm for 1D maximum subarray search. The first algorithm is a novel, computationally superior branch-and-bound method where the worst case complexity is reduced to O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). Experiments on the PASCAL VOC 2006 data set demonstrate that this method is significantly and consistently faster (approximately 30 times faster on average) than the original ESS. Our second algorithm is an approximate algorithm based on alternating search, whose computational complexity is typically O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Experiments shows that (on average) it is 30 times faster again than our first algorithm, or 900 times faster than ESS. It is thus well-suited for real time object detection.
Learning based automatic face annotation for arbitrary poses and expressions from frontal images only
Authors: Akshay Asthana, Roland Goecke, Novi Quadrianto, Tom Gedeon
Venue: CVPR
Year: 2009

Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases.
Visual tracking with online Multiple Instance Learning
Authors: Boris Babenko, Ming-Hsuan Yang 0001, Serge J. Belongie
Venue: CVPR
Year: 2009

In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.
Contextual restoration of severely degraded document images
Authors: Jyotirmoy Banerjee, Anoop M. Namboodiri, C. V. Jawahar
Venue: CVPR
Year: 2009

We propose an approach to restore severely degraded document images using a probabilistic context model. Unlike traditional approaches that use previously learned prior models to restore an image, we are able to learn the text model from the degraded document itself, making the approach independent of script, font, style, etc. We model the contextual relationship using an MRF. The ability to work with larger patch sizes allows us to deal with severe degradations including cuts, blobs, merges and vandalized documents. Our approach can also integrate document restoration and super-resolution into a single framework, thus directly generating high quality images from degraded documents. Experimental results show significant improvement in image quality on document images collected from various sources including magazines and books, and comprehensively demonstrate the robustness and adaptability of the approach. It works well with document collections such as books, even with severe degradations, and hence is ideally suited for repositories such as digital libraries.
Visibility constraints on features of 3D objects
Authors: Ronen Basri, Pedro F. Felzenszwalb, Ross B. Girshick, David W. Jacobs, Caroline J. Klivans
Venue: CVPR
Year: 2009

To recognize three-dimensional objects it is important to model how their appearances can change due to changes in viewpoint. A key aspect of this involves understanding which object features can be simultaneously visible under different viewpoints. We address this problem in an image-based framework, in which we use a limited number of images of an object taken from unknown viewpoints to determine which subsets of features might be simultaneously visible in other views. This leads to the problem of determining whether a set of images, each containing a set of features, is consistent with a single 3D object. We assume that each feature is visible from a disk of viewpoints on the viewing sphere. In this case we show the problem is NP-hard in general, but can be solved efficiently when all views come from a circle on the viewing sphere. We also give iterative algorithms that can handle noisy data and converge to locally optimal solutions in the general case. Our techniques can also be used to recover viewpoint information from the set of features that are visible in different images. We show that these algorithms perform well both on synthetic data and images from the COIL dataset.
Fast human detection in crowded scenes by contour integration and local shape estimation
Authors: Csaba Beleznai, Horst Bischof
Venue: CVPR
Year: 2009

The complexity of human detection increases significantly with a growing density of humans populating a scene. This paper presents a Bayesian detection framework using shape and motion cues to obtain a maximum a posteriori (MAP) solution for human configurations consisting of many, possibly occluded pedestrians viewed by a stationary camera. The paper contains two novel contributions for the human detection task: 1. computationally efficient detection based on shape templates using contour integration by means of integral images which are built by oriented string scans; (2) a non-parametric approach using an approximated version of the shape context descriptor which generates informative object parts and infers the presence of humans despite occlusions. The outputs of the two detectors are used to generate a spatial configuration of hypothesized human body locations. The configuration is iteratively optimized while taking into account the depth ordering and occlusion status of the hypotheses. The method achieves fast computation times even in complex scenarios with a high density of people. Its validity is demonstrated on a substantial amount of image data using the CAVIAR and our own datasets. Evaluation results and comparison with state of the art are presented.