OpenRaiser/Intern-Atlas huggingface dataset:
A study on automatic age estimation using a large database
Authors: Guodong Guo, Guowang Mu, Yun Fu 0001, Charles R. Dyer, Thomas S. Huang
Venue: ICCV
Year: 2009

In this paper we study some problems related to human age estimation using a large database. First, we study the influence of gender on age estimation based on face representations that combine biologically-inspired features with manifold learning techniques. Second, we study age estimation using smaller gender and age groups rather than on all ages. Significant error reductions are observed in both cases. Based on these results, we designed three frameworks for automatic age estimation that exhibit high performance. Unlike previous methods that require manual separation of males and females prior to age estimation, our work is the first to estimate age automatically on a large database. Furthermore, a data fusion approach is proposed using one of the frameworks, which gives an age estimation error more than 40% smaller than previous methods.
Video scene categorization by 3D hierarchical histogram matching
Authors: Paritosh Gupta, Sai Sankalp Arrabolu, Mathew Brown, Silvio Savarese
Venue: ICCV
Year: 2009

In this paper we present a new method for categorizing video sequences capturing different scene classes. This can be seen as a generalization of previous work on scene classification from single images. A scene is represented by a collection of 3D points with an appearance based codeword attached to each point. The cloud of points is recovered by using a robust SFM algorithm applied on the video sequence. A hierarchical structure of histograms located at different locations and at different scales is used to capture the typical spatial distribution of 3D points and codewords in the working volume. The scene is classified by SVM equipped with a histogram matching kernel, similar to. Results on a challenging dataset of 5 scene categories show competitive classification accuracy and superior performance with respect to a state-of-the-art 2D pyramid matching methods applied to individual image frames.
Is dual linear self-calibration artificially ambiguous?
Authors: Pierre Gurdjos, Adrien Bartoli, Peter F. Sturm
Venue: ICCV
Year: 2009

This purely theoretical work investigates the problem of artificial singularities in camera self-calibration. Self-calibration allows one to upgrade a projective reconstruction to metric and has a concise and well-understood formulation based on the Dual Absolute Quadric (DAQ), a rank-3 quadric envelope satisfying (nonlinear) ‘spectral constraints’: it must be positive of rank 3. The practical scenario we consider is the one of square pixels, known principal point and varying unknown focal length, for which generic Critical Motion Sequences (CMS) have been thoroughly derived. The standard linear self-calibration algorithm uses the DAQ paradigm but ignores the spectral constraints. It thus has artificial CMSs, which have barely been studied so far. We propose an algebraic model of singularities based on the confocal quadric theory. It allows to easily derive all types of CMSs. We first review the already known generic CMSs, for which any self-calibration algorithm fails. We then describe all CMSs for the standard linear self-calibration algorithm; among those are artificial CMSs caused by the above spectral constraints being neglected. We then show how to detect CMSs. If this is the case it is actually possible to uniquely identify the correct self-calibration solution, based on a notion of signature of quadrics. The main conclusion of this paper is that a posteriori enforcing the spectral constraints in linear self-calibration is discriminant enough to resolve all artificial CMSs.
Semi-automatic stereo extraction from video footage
Authors: Moshe Guttmann, Lior Wolf, Daniel Cohen-Or
Venue: ICCV
Year: 2009

We present a semi-automatic system that converts conventional video shots to stereoscopic video pairs. The system requires just a few user-scribbles in a sparse set of frames. The system combines a diffusion scheme, which takes into account the local saliency and the local motion at each video location, coupled with a classification scheme that assigns depth to image patches. The system tolerates both scene motion and camera motion. In typical shots, containing hundreds of frames, even in the face of significant motion, it is enough to mark scribbles on the first and last frames of the shot. Once marked, plausible stereo results are obtained in a matter of seconds, leading to a scalable video conversion system. Finally, we validate our results with ground truth stereo video.
Tracking a hand manipulating an object
Authors: Henning Hamer, Konrad Schindler, Esther Koller-Meier, Luc Van Gool
Venue: ICCV
Year: 2009

We present a method for tracking a hand while it is interacting with an object. This setting is arguably the one where hand-tracking has most practical relevance, but poses significant additional challenges: strong occlusions by the object as well as self-occlusions are the norm, and classical anatomical constraints need to be softened due to the external forces between hand and object. To achieve robustness to partial occlusions, we use an individual local tracker for each segment of the articulated structure. The segments are connected in a pairwise Markov random field, which enforces the anatomical hand structure through soft constraints on the joints between adjacent segments. The most likely hand configuration is found with belief propagation. Both range and color data are used as input. Experiments are presented for synthetic data with ground truth and for real data of people manipulating objects.
Active Appearance Models with Rotation Invariant Kernels
Authors: Onur C. Hamsici, Aleix M. Martínez
Venue: ICCV
Year: 2009

2D Active Appearance Models (AAM) and 3D Morphable Models (3DMM) are widely used techniques. AAM provide a fast fitting process, but may represent unwanted 3D transformations unless strictly constrained not to do so. The reverse is true for 3DMM. The two approaches also require of a pre-alignment of their 2D or 3D shapes before the modeling can be carried out which may lead to errors. Furthermore, current models are insufficient to represent nonlinear shape and texture variations. In this paper, we derive a new approach that can model nonlinear changes in examples without the need of a pre-alignment step. In addition, we show how the proposed approach carries the above mentioned advantages of AAM and 3DMM. To achieve this goal, we take advantage of the inherent properties of complex spherical distributions, which provide invariance to translation, scale and rotation. To reduce the complexity of parameter estimation we take advantage of a recent result that shows how to estimate spherical distributions using their Euclidean counterpart, e.g., the Gaussians. This leads to the definition of Rotation Invariant Kernels (RIK) for modeling nonlinear shape changes. We show the superiority of our algorithm to AAM in several face datasets. We also show how the derived algorithm can be used to model complex 3D facial expression changes observed in American Sign Language (ASL).
Optimal multiple surfaces searching for video/image resizing - a graph-theoretic approach
Authors: Dongfeng Han, Xiaodong Wu 0001, Milan Sonka
Venue: ICCV
Year: 2009

Content-aware video/image resizing is of increasing relevance to allow high-quality image and video resizing to be displayed on devices with different resolution. In this paper, we present a novel algorithm to find multiple 3-D surfaces simultaneously with globally optimal solution for video/image resizing. Our algorithm is based on graph theory and it first analyzes the video/image data to define the energy value for each voxel. Then, a 4-D graph is constructed and the costs are assigned according to the energy values. Finally, multiple 3-D surfaces are detected by a global optimization process which can be solved via s-t graph cuts. By removing or inserting these multiple 3-D surfaces, content-aware video/image resizing is achieved. We also have proved that our algorithm can find the globally optimal solution for crossing surfaces problem, in which several surfaces can cross each other. The proposed method is demonstrated on a variety of video/image data and compared to the state of the art in video/image resizing.
Combining efficient object localization and image classification
Authors: Hedi Harzallah, Frédéric Jurie, Cordelia Schmid
Venue: ICCV
Year: 2009

In this paper we present a combined approach for object localization and classification. Our contribution is twofold. (a) A contextual combination of localization and classification which shows that classification can improve detection and vice versa. (b) An efficient two stage sliding window object localization method that combines the efficiency of a linear classifier with the robustness of a sophisticated non-linear one. Experimental results evaluate the parameters of our two stage sliding window approach and show that our combined object localization and classification methods outperform the state-of-the-art on the PASCAL VOC 2007 and 2008 datasets.
Active subspace learning
Authors: Xiaofei He 0001, Deng Cai 0001
Venue: ICCV
Year: 2009

Many previous studies have shown that naturally occurring data cannot possibly fill up the high dimensional space uniformly, rather it must concentrate around lower dimensional structure. The typical supervised subspace learning algorithms to discover this low dimensional structure include Linear Discriminant Analysis (LDA). For LDA, the training data points are usually pre-given. However, in some real world applications like relevance feedback image retrieval, there is opportunity to interact with the user and actively select the training points for labeling. In this paper, we propose a novel active subspace learning algorithm which selects the most informative data points and uses them for learning an optimal subspace. Using techniques from experimental design, we discuss how to perform data selection in supervised or semi-supervised subspace learning by minimizing the expected error. Experiments on image retrieval show improvement over state-of-the-art methods.
SURF Tracking
Authors: Wei He, Takayoshi Yamashita, Hongtao Lu, Shihong Lao
Venue: ICCV
Year: 2009

Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by a set of local invariant features, whose motions are observed by a feature correspondence process. A generative model is proposed to depict the relationship between local feature motions and object global motion, whose parameters are learned efficiently by an on-line EM algorithm. And the object global motion is estimated in term of maximum likelihood of observations. Then an updating mechanism is employed to adapt object representation. Experiments show that our framework is flexible and robust in dealing with appearance changes, background clutter, illumination changes and occlusion.
Bayesian selection of scaling laws for motion modeling in images
Authors: Patrick Héas, Étienne Mémin, Dominique Heitz, Pablo D. Mininni
Venue: ICCV
Year: 2009

Based on scaling laws describing the statistical structure of turbulent motion across scales, we propose a multiscale and non-parametric regularizer for optic-flow estimation. Regularization is achieved by constraining motion increments to behave through scales as the most likely self-similar process given some image data. In a first level of inference, the hard constrained minimization problem is optimally solved by taking advantage of lagrangian duality. It results in a collection of first-order regularizers acting at different scales. This estimation is non-parametric since the optimal regularization parameters at the different scales are obtained by solving the dual problem. In a second level of inference, the most likely self-similar model given the data is optimally selected by maximization of Bayesian evidence. The motion estimator accuracy is first evaluated on a synthetic image sequence of simulated bi-dimensional turbulence and then on a real meteorological image sequence. Results obtained with the proposed physical based approach exceeds the best state of the art results. Furthermore, selecting from images the most evident multiscale motion model enables the recovery of physical quantities, which are of major interest for turbulence characterization.
Simultaneous color consistency and depth map estimation for radiometrically varying stereo images
Authors: Yong Seok Heo, Kyoung Mu Lee, Sang Uk Lee
Venue: ICCV
Year: 2009

In this paper, we propose a new method that infers accurate depth maps and color-consistent images between radiometrically varying stereo images, simultaneously. In general, stereo matching and performing color consistency between stereo images are a chicken-and-egg problem. Color consistency enhances the performance of stereo matching, while accurate correspondences from stereo disparities improve color consistency between stereo images. We devise a new iterative framework in which these two processes can boost each other. For robust stereo matching, we utilize the mutual information-based method combined with the SIFT descriptor from which we can estimate the joint pdf in log-chromaticity color space. From this joint pdf, we can estimate a linear relationship between the corresponding pixels in stereo images. Using this linear relationship and the estimated depth maps, we devise a stereo color histogram equalization method to make color-consistent stereo images which conversely boost the disparity map estimation. Experimental results show that our method produces both accurate depth maps and color-consistent stereo images even for stereo images with severe radiometric differences.
An algebraic approach to affine registration of point sets
Authors: Jeffrey Ho, Adrian M. Peter, Anand Rangarajan 0001, Ming-Hsuan Yang 0001
Venue: ICCV
Year: 2009

This paper proposes a new affine registration algorithm for matching two point sets in IR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> or IR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The input point sets are represented as probability density functions, using either Gaussian mixture models or discrete density models, and the problem of registering the point sets is treated as aligning the two distributions. Since polynomials transform as symmetric tensors under an affine transformation, the distributions' moments, which are the expected values of polynomials, also transform accordingly. Therefore, instead of solving the harder problem of aligning the two distributions directly, we solve the softer problem of matching the distributions' moments. By formulating a least-squares problem for matching moments of the two distributions up to degree three, the resulting cost function is a polynomial that can be efficiently optimized using techniques originated from algebraic geometry: the global minimum of this polynomial can be determined by solving a system of polynomial equations. The algorithm is robust in the presence of noises and outliers, and we validate the proposed algorithm on a variety of point sets with varying degrees of deformation and noise.
An efficient algorithm for Co-segmentation
Authors: Dorit S. Hochbaum, Vikas Singh
Venue: ICCV
Year: 2009

This paper is focused on the Co-segmentation problem [1] - where the objective is to segment a similar object from a pair of images. The background in the two images may be arbitrary; therefore, simultaneous segmentation of both images must be performed with a requirement that the appearance of the two sets of foreground pixels in the respective images are consistent. Existing approaches [1, 2] cast this problem as a Markov Random Field (MRF) based segmentation of the image pair with a regularized difference of the two histograms - assuming a Gaussian prior on the foreground appearance [1] or by calculating the sum of squared differences [2]. Both are interesting formulations but lead to difficult optimization problems, due to the presence of the second (histogram difference) term. The model proposed here bypasses measurement of the histogram differences in a direct fashion; we show that this enables obtaining efficient solutions to the underlying optimization model. Our new algorithm is similar to the existing methods in spirit, but differs substantially in that it can be solved to optimality in polynomial time using a maximum flow procedure on an appropriately constructed graph. We discuss our ideas and present promising experimental results.
Label set perturbation for MRF based neuroimaging segmentation
Authors: Dylan Hower, Vikas Singh, Sterling C. Johnson
Venue: ICCV
Year: 2009

Graph-cuts based algorithms are effective for a variety of segmentation tasks in computer vision. Ongoing research is focused toward making the algorithms even more general, as well as to better understand their behavior with respect to issues such as the choice of the weighting function and sensitivity to placement of seeds. In this paper, we investigate in the context of neuroimaging segmentation, the sensitivity/stability of the solution with respect to the input “labels” or “seeds”. In particular, as a form of parameter learning, we are interested in the effect of allowing the given set of labels (and consequently, the response/statistics of the weighting function) to vary for obtaining lower energy segmentation solutions. This perturbation leads to a “refined” label set (or parameters) better suited to the input image, yielding segmentations that are less sensitive to the set of labels or seeds provided. Our proposed algorithm (using Parametric Pseudoflow) yields improvements over graph-cuts based segmentation with a fixed set of labels. We present experiments on about 450 3-D brain image volumes demonstrating the efficacy of the algorithm.
Action detection in complex scenes with spatial and temporal ambiguities
Authors: Yuxiao Hu 0001, Liangliang Cao, Fengjun Lv, Shuicheng Yan, Yihong Gong, Thomas S. Huang
Venue: ICCV
Year: 2009

In this paper, we investigate the detection of semantic human actions in complex scenes. Unlike conventional action recognition in well-controlled environments, action detection in complex scenes suffers from cluttered backgrounds, heavy crowds, occluded bodies, and spatial-temporal boundary ambiguities caused by imperfect human detection and tracking. Conventional algorithms are likely to fail with such spatial-temporal ambiguities. In this work, the candidate regions of an action are treated as a bag of instances. Then a novel multiple-instance learning framework, named SMILE-SVM (Simulated annealing Multiple Instance LEarning Support Vector Machines), is presented for learning human action detector based on imprecise action locations. SMILE-SVM is extensively evaluated with satisfactory performances on two tasks: 1) human action detection on a public video action database with cluttered backgrounds, and 2) a real world problem of detecting whether the customers in a shopping mall show an intention to purchase the merchandise on shelf (even if they didn't buy it eventually). In addition, the complementary nature of motion and appearance features in action detection are also validated, demonstrating a boosted performance in our experiments.
A robust elastic and partial matching metric for face recognition
Authors: Gang Hua 0001, Amir Akbarzadeh
Venue: ICCV
Year: 2009

We present a robust elastic and partial matching metric for face recognition. To handle challenges such as pose, facial expression and partial occlusion, we enable both elastic and partial matching by computing a part based face representation. In which N local image descriptors are extracted from densely sampled overlapping image patches. We then define a distance metric where each descriptor in one face is matched against its spatial neighborhood in the other face and the minimal distance is recorded. For implicit partial matching, the list of all minimal distances are sorted in ascending order and the distance at the αN-th position is picked up as the final distance. The parameter 0 ≤ α ≤ 1 controls how much occlusion, facial expression changes, or pixel degradations we would allow. The optimal parameter values of this new distance metric are extensively studied and identified with real-life photo collections. We also reveal that filtering the face image by a simple difference of Gaussian brings significant robustness to lighting variations and beats the more utilized self-quotient image. Extensive evaluations on face recognition benchmarks show that our method is leading or is competitive in performance when compared to state-of-the-art.
Landmark-based sparse color representations for color transfer
Authors: Tzu-Wei Huang, Hwann-Tzong Chen
Venue: ICCV
Year: 2009

We present a novel image representation that characterizes a color image by an intensity image and a small number of color pixels. Our idea is based on solving an inverse problem of colorization: Given a color image, we seek to obtain an intensity image and a small subset of color pixels, which are called landmark pixels, so that the input color image can be recovered faithfully using the intensity image and the color cues provided by the selected landmark pixels. We develop an algorithm to derive the landmark-based sparse color representations from color images, and use the representations in the applications of color transfer and color correction. The computational cost for these applications is low owing to the sparsity of the proposed representation. The landmark-based representation is also preferable to statistics-based representations, e.g. color histograms and Gaussian mixture models, when we need to reconstruct the color image from a given representation.
Simultaneous photometric invariance and shape recovery
Authors: Cong Phuoc Huynh, Antonio Robles-Kelly
Venue: ICCV
Year: 2009

In this paper, we identify the constraints under which the generally ill-posed problem of simultaneous recovery of surface shape and its photometric invariants can be rendered tractable. We examine the cases where a single or more images are acquired using different lighting directions with known illuminant power. Given these conditions, we state the constraints upon which the recovery of the surface geometry and its photometric parameters can be estimated. With these constraints, we then show how the recovery process may be formulated as an optimisation algorithm which aims to fit the reflectance models under study to the image reflectance. The approach presented here is general and can be applied to a family of reflectance models that are based on the Fresnel reflection theory. Thus, we provide a theoretical and computational background for recovering shape, material index of refraction and microscopic roughness from multi-spectral images.
Learning actions from the Web
Authors: Nazli Ikizler-Cinbis, Ramazan Gokberk Cinbis, Stan Sclaroff
Venue: ICCV
Year: 2009

This paper proposes a generic method for action recognition in uncontrolled videos. The idea is to use images collected from the Web to learn representations of actions and use this knowledge to automatically annotate actions in videos. Our approach is unsupervised in the sense that it requires no human intervention other than the text querying. Its benefits are two-fold: 1) we can improve retrieval of action images, and 2) we can collect a large generic database of action poses, which can then be used in tagging videos. We present experimental evidence that using action images collected from the Web, annotating actions is possible.