OpenRaiser/Intern-Atlas huggingface dataset:
Domain adaptation for object recognition: An unsupervised approach
Authors: Raghuraman Gopalan, Ruonan Li, Rama Chellappa
Venue: ICCV
Year: 2011

Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.
Recursive MDL via graph cuts: Application to segmentation
Authors: Lena Gorelick, Andrew Delong, Olga Veksler, Yuri Boykov
Venue: ICCV
Year: 2011

We propose a novel patch-based image representation that is useful because it (1) inherently detects regions with repetitive structure at multiple scales and (2) yields a parameterless hierarchical segmentation. We describe an image by breaking it into coherent regions where each region is well-described (easily reconstructed) by repeatedly instantiating a patch using a set of simple transformations. In other words, a good segment is one that has sufficient repetition of some pattern, and a patch is useful if it contains a pattern that is repeated in the image. Our criterion is naturally expressed by the well-established minimum description length (MDL) principle. MDL prefers spatially coherent regions with consistent appearance and avoids parameter tuning. We minimize the description length (in bits) of the image by encoding it with patches. Because a patch is itself an image, we measure its description length by applying the same idea recursively: encode a patch by breaking it into regions described by yet simpler patches. The resulting hierarchy of inter-dependent patches naturally leads to a hierarchical segmentation. We minimize description length over our class of image representations (all patch hierarchies / partitions). We formulate this problem as a recursive multi-label energy. Existing optimization techniques are either inapplicable or get stuck in poor local minima. We propose a new hierarchical fusion (HF) algorithm for energies containing a hierarchy of 'label costs'. Our algorithm is a contribution in itself and should be useful for this new and difficult class of energies.
Kernel non-rigid structure from motion
Authors: Paulo F. U. Gotardo, Aleix M. Martínez
Venue: ICCV
Year: 2011

Non-rigid structure from motion (NRSFM) is a difficult, underconstrained problem in computer vision. The standard approach in NRSFM constrains 3D shape deformation using a linear combination of K basis shapes; the solution is then obtained as the low-rank factorization of an input observation matrix. An important but overlooked problem with this approach is that non-linear deformations are often observed; these deformations lead to a weakened low-rank constraint due to the need to use additional basis shapes to linearly model points that move along curves. Here, we demonstrate how the kernel trick can be applied in standard NRSFM. As a result, we model complex, deformable 3D shapes as the outputs of a non-linear mapping whose inputs are points within a low-dimensional shape space. This approach is flexible and can use different kernels to build different non-linear models. Using the kernel trick, our model complements the low-rank constraint by capturing non-linear relationships in the shape coefficients of the linear model. The net effect can be seen as using non-linear dimensionality reduction to further compress the (shape) space of possible solutions.
Segmentation from a box
Authors: Leo J. Grady, Marie-Pierre Jolly, Aaron R. Seitz
Venue: ICCV
Year: 2011

Drawing a box around an intended segmentation target has become both a popular user interface and a common output for learning-driven detection algorithms. Despite the ubiquity of using a box to define a segmentation target, it is unclear in the literature whether a box is sufficient to define a unique segmentation or whether segmentation from a box is ill-posed without higher-level (semantic) knowledge of the intended target. We examine this issue by conducting a study of 14 subjects who are asked to segment a boxed target in a set of 50 real images for which they have no semantic attachment. We find that the subjects do indeed perceive and trace almost the same segmentations as each other, despite the inhomogeneity of the image intensities, irregular shapes of the segmentation targets and weakness of the target boundaries. Since the subjects produce the same segmentation, we conclude that the problem is well-posed and then provide a new segmentation algorithm from a box which achieves results close to the perceived target.
Material-specific user colour profiles from imaging spectroscopy data
Authors: Lin Gu 0003, Cong Phuoc Huynh, Antonio Robles-Kelly, Jun Zhou 0001
Venue: ICCV
Year: 2011

In this paper, we present a method which permits the creation of user colour preferences for object materials and lights in the scene making use of imaging spectroscopy data. To do this, we build upon the heterogeneous nature of the scene by imposing consistency over object materials so as to allow for small compositional variations across objects in the image. Once the consistency has been imposed, we aim at maximising the quality of the images under consideration based upon user input. This provides the flexibility necessary to utilise user profiles for the automatic processing of real world imagery while avoiding undesirable effects encountered when colour images are produced. We provide results on real-world imagery and illustrate how the method can be used to produce material-specific colours based upon user input.
Linear time offline tracking and lower envelope algorithms
Authors: Steve Gu, Ying Zheng, Carlo Tomasi
Venue: ICCV
Year: 2011

Offline tracking of visual objects is particularly helpful in the presence of significant occlusions, when a frame-by-frame, causal tracker is likely to lose sight of the target. In addition, the trajectories found by offline tracking are typically smoother and more stable because of the global optimization this approach entails. In contrast with previous work, we show that this global optimization can be performed in O(MNT) time for T frames of video at M × N resolution, with the help of the generalized distance transform developed by Felzenszwalb and Huttenlocher [13]. Recognizing the importance of this distance transform, we extend the computation to a more general lower envelope algorithm in certain heterogeneous l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -distance metric spaces. The generalized lower envelope algorithm is of complexity O(MN(M+N)) and is useful for a more challenging offline tracking problem. Experiments show that trajectories found by offline tracking are superior to those computed by online tracking methods, and are computed at 100 frames per second.
Digital anti-aging in face images
Authors: Guodong Guo
Venue: ICCV
Year: 2011

We study a problem called digital facial anti-aging, which aims at making human faces look younger in digital photos. This novel problem is different from the traditional age synthesis where new faces are synthesized at older ages using other example face images. In contrast, our facial anti-aging works on a single digital photo without using any other faces. The proposed system contains several modules. First, an input color face image is decomposed into three layers: face structure, aging detail, and color. Second, the specular highlight in the original face image is recovered. Third, the face structure, color, and the recovered specular highlight are combined to form an anti-aging face image. Further, the system can deal with hair coloring and eyebrow change if needed, since these factors influence human judges of ages. Our approach keeps facial identities and delivers high-quality outputs. We show that the anti-aging system can be built based on adapting and integrating the state-of-the-art methods that were originally proposed to solve other problems. The anti-aging faces are evaluated by humans to demonstrate the performance.
Delta-Dual Hierarchical Dirichlet Processes: A pragmatic abnormal behaviour detector
Authors: Tom S. F. Haines, Tao Xiang 0002
Venue: ICCV
Year: 2011

In the security domain a key problem is identifying rare behaviours of interest. Training examples for these behaviours may or may not exist, and if they do exist there will be few examples, quite probably one. We present a novel weakly supervised algorithm that can detect behaviours that either have never before been seen or for which there are few examples. Global context is modelled, allowing the detection of abnormal behaviours that in isolation appear normal. Pragmatic aspects are considered, such that no parameter tuning is required and real time performance is achieved.
Video Primal Sketch: A generic middle-level representation of video
Authors: Zhi Han, Zongben Xu, Song-Chun Zhu
Venue: ICCV
Year: 2011

This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving primitives to explicitly represent moving corners, lines, feature points, etc., ii) FRAME/MRF model with spatio-temporal filters to implicitly represent textured motion, such as water and fire, by matching feature statistics, i.e. histograms. This paper makes three contributions: i) learning a dictionary of video primitives as parametric generative model; ii) studying the Spatio-Temporal FRAME (ST-FRAME) model for modeling and synthesizing textured motion; and iii) developing a parsimonious hybrid model for generic video representation. VPS selects the proper representation automatically and is compatible with high-level action representations. In the experiments, we synthesize a series of dynamic textures, reconstruct real videos and show varying VPS over the change of densities causing by the scale transition in videos.
Struck: Structured output tracking with kernels
Authors: Sam Hare, Amir Saffari, Philip H. S. Torr
Venue: ICCV
Year: 2011

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (accurate estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we are able to avoid the need for an intermediate classification step. Our method uses a kernelized structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow for real-time application, we introduce a budgeting mechanism which prevents the unbounded growth in the number of support vectors which would otherwise occur during tracking. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased performance.
Diffuse reflectance imaging with astronomical applications
Authors: Samuel W. Hasinoff, Anat Levin, Philip R. Goode, William T. Freeman
Venue: ICCV
Year: 2011

Diffuse objects generally tell us little about the surrounding lighting, since the radiance they reflect blurs together incident lighting from many directions. In this paper we discuss how occlusion geometry can help invert diffuse reflectance to recover lighting or surface albedo. Self-occlusion in the scene can be regarded as a form of coding, creating high frequencies that improve the conditioning of diffuse light transport. Our analysis builds on a basic observation that diffuse reflectors with sufficiently detailed geometry can fully resolve the incident lighting. Using a Bayesian framework, we propose a novel reconstruction method based on high-resolution photography, taking advantage of visibility changes near occlusion boundaries. We also explore the limits of single-pixel observations as the diffuse reflector (and potentially the lighting) vary over time. Diffuse reflectance imaging is particularly relevant for astronomy applications, where diffuse reflectors arise naturally but the incident lighting and camera position cannot be controlled. To test our approaches, we first study the feasibility of using the moon as a diffuse reflector to observe the earth as seen from space. Next we present a reconstruction of Mars using historical photometry measurements not previously used for this purpose. As our results suggest, diffuse reflectance imaging expands our notion of what can qualify as a camera.
Fast template matching in non-linear tone-mapped images
Authors: Yacov Hel-Or, Hagit Hel-Or, Eyal David
Venue: ICCV
Year: 2011

We propose a fast pattern matching scheme termed Matching by Tone Mapping (MTM) which allows matching under non-linear tone mappings. We show that, when tone mapping is approximated by a piecewise constant function, a fast computational scheme is possible requiring computational time similar to the fast implementation of Normalized Cross Correlation (NCC). In fact, the MTM measure can be viewed as a generalization of the NCC for non-linear mappings and actually reduces to NCC when mappings are restricted to be linear. The MTM is shown to be invariant to non-linear tone mappings, and is empirically shown to be highly discriminative and robust to noise.
Fast removal of non-uniform camera shake
Authors: Michael Hirsch 0001, Christian J. Schuler, Stefan Harmeling, Bernhard Schölkopf
Venue: ICCV
Year: 2011

Camera shake leads to non-uniform image blurs. State-of-the-art methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this paper we develop a forward model based on the efficient filter flow framework, incorporating the particularities of camera shake, and show how an efficient algorithm for blur removal can be obtained. Comprehensive comparisons on a number of real-world blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results.
Learning a mixture of sparse distance metrics for classification and dimensionality reduction
Authors: Yi Hong, Quannan Li, Jiayan Jiang, Zhuowen Tu
Venue: ICCV
Year: 2011

This paper extends the neighborhood components analysis method (NCA) to learning a mixture of sparse distance metrics for classification and dimensionality reduction. We emphasize two important properties in the recent learning literature, locality and sparsity, and (1) pursue a set of local distance metrics by maximizing a conditional likelihood of observed data; and (2) add l1-norm of eigenvalues of the distance metric to favor low rank matrices of fewer parameters. Experimental results on standard UCI machine learning datasets, face recognition datasets, and image categorization datasets demonstrate the feasibility of our approach for both distance metric learning and dimensionality reduction.
Image representation by active curves
Authors: Wenze Hu, Ying Nian Wu, Song-Chun Zhu
Venue: ICCV
Year: 2011

This paper proposes a sparse image representation using deformable templates of simple geometric structures that are commonly observed in images of natural scenes. These deformable templates include active curve templates and active corner templates. An active curve template is a composition of Gabor wavelet elements placed with equal spacing on a straight line segment or a circular arc segment of constant curvature, where each Gabor wavelet element is allowed to locally shift its location and orientation, so that the original line and arc segment of the active curve template can be deformed to fit the observed image. An active corner or angle template is a composition of two active curve templates that share a common end point, and the active curve templates are allowed to vary their overall lengths and curvatures, so that the original corner template can deform to match the observed image. This paper then proposes a hierarchical computational architecture of summax maps that pursues a sparse representation of an image by selecting a small number of active curve and corner templates from a dictionary of all such templates. Experiments show that the proposed method is capable of finding sparse representations of natural images. It is also shown that object templates can be learned by selecting and composing active curve and corner templates.
Optical flow estimation using learned sparse model
Authors: Kui Jia, Xiaogang Wang 0001, Xiaoou Tang
Venue: ICCV
Year: 2011

Optical flow estimation is a fundamental and ill-posed problem in computer vision. To recover a dense flow field, appropriate spatial constraints have to be enforced. Recent advances exploit higher order spatial regularization, and achieve the top performance on the Middlebury benchmark. In this work, we revisit learning-based approach, and propose a learned sparse model to patch-wisely regularize the flow field. In particular, our method is based on multi-scale spatial regularization, which benefits from first-order spatial regularity and our learned, higher order sparse model. To obtain accurate flow estimation, we propose a sequential optimization scheme to solve the corresponding energy minimization problem. Moreover, as the errors in intermediate flow estimates are usually dense with large variations, we further propose flow-driven and image-driven approaches to address the problem of outliers. Experiments on the Middlebury benchmark show that our method is competitive with the state-of-the-art.
Generalized subgraph preconditioners for large-scale bundle adjustment
Authors: Yong-Dian Jian, Doru-Cristian Balcan, Frank Dellaert
Venue: ICCV
Year: 2011

We present a generalized subgraph preconditioning (GSP) technique to solve large-scale bundle adjustment problems efficiently. In contrast with previous work which uses either direct or iterative methods as the linear solver, GSP combines their advantages and is significantly faster on large datasets. Similar to [11], the main idea is to identify a sub-problem (subgraph) that can be solved efficiently by sparse factorization methods and use it to build a preconditioner for the conjugate gradient method. The difference is that GSP is more general and leads to much more effective preconditioners. We design a greedy algorithm to build subgraphs which have bounded maximum clique size in the factorization phase, and also result in smaller condition numbers than standard preconditioning techniques. When applying the proposed method to the “bal” datasets [1], GSP displays promising performance.
Multiscale, curvature-based shape representation for surfaces
Authors: Ruirui Jiang, Xianfeng Gu
Venue: ICCV
Year: 2011

This paper presents a multiscale, curvature-based shape representation technique for general genus zero closed surfaces. The method is invariant under rotation, translation, scaling, or general isometric deformations; it is robust to noise and preserves intrinsic symmetry. The method is a direct generalization of the Curvature Scale Space (CSS) shape descriptor for planar curves. In our method, the Riemannian metric of the surface is deformed under Ricci flow, such that the Gaussian curvature evolves according to a heat diffusion process. Eventually the surface becomes a sphere with constant positive curvature everywhere. The evolution of zero curvature curves on the surface is utilized as the shape descriptor. Our experimental results on a 3D geometric database with about 80 shapes demonstrate the efficiency and efficacy of the method.
Multi-view repetitive structure detection
Authors: Nianjuan Jiang, Ping Tan, Loong Fah Cheong
Venue: ICCV
Year: 2011

Symmetry, especially repetitive structures in architecture are universally demonstrated across countries and cultures. Existing detection methods mainly focus on the detection of planar patterns from a single image. It is difficult to apply them to detect repetitive structures in architecture, which abounds with non-planar 3D repetitive elements (such as balconies and windows) and curved surfaces. We study the repetitive structure detection problem from multiple images of such architecture. Our method jointly analyzes these images and a set of 3D points reconstructed from them by structure-from-motion algorithms. 3D points help to rectify geometric deformations and hypothesize possible lattice structures, while images provide denser color and texture information to evaluate and confirm these hypotheses. In the experiments, we compare our method with existing algorithm. We also show how our results might be used to assist image-based modeling.
Stereo reconstruction using high order likelihood
Authors: Ho Yub Jung, Kyoung Mu Lee, Sang Uk Lee
Venue: ICCV
Year: 2011

Under the popular Bayesian approach, a stereo problem can be formulated by defining likelihood and prior. Likelihoods are often associated with unary terms and priors are defined by pair-wise or higher order cliques in Markov random field (MRF). In this paper, we propose to use high order likelihood model in stereo. Numerous conventional patch based matching methods such as normalized cross correlation, Laplacian of Gaussian, or census filters are designed under the naive assumption that all the pixels of a patch have the same disparities. However, patch-wise cost can be formulated as higher order cliques for MRF so that the matching cost is a function of image patch's disparities. A patch obtained from the projected image by a disparity map should provide a better match without the blurring effect around disparity discontinuities. Among patch-wise high order matching costs, the census filter approach can be easily reduced to pair-wise cliques. The experimental results on census filter-based high order likelihood demonstrate the advantages of high order likelihood over independent identically distributed unary model.