OpenRaiser/Intern-Atlas huggingface dataset:
Active clustering of document fragments using information derived from both images and catalogs
Authors: Lior Wolf, Lior Litwak, Nachum Dershowitz, Roni Shweka, Yaacov Choueka
Venue: ICCV
Year: 2011

Many significant historical corpora contain leaves that are mixed up and no longer bound in their original state as multi-page documents. The reconstruction of old manuscripts from a mix of disjoint leaves can therefore be of paramount importance to historians and literary scholars. Previously, it was shown that visual similarity provides meaningful pair-wise similarities between handwritten leaves. Here, we go a step further and suggest a semiautomatic clustering tool that helps reconstruct the original documents. The proposed solution is based on a graphical model that makes inferences based on catalog information provided for each leaf as well as on the pairwise similarities of handwriting. Several novel active clustering techniques are explored, and the solution is applied to a significant part of the Cairo Genizah, where the problem of joining leaves remains unsolved even after a century of extensive study by hundreds of scholars.
Parallelizable inpainting and refinement of diffeomorphisms using Beltrami holomorphic flow
Authors: Tsz Wai Wong, Xianfeng Gu, Tony F. Chan, Lok Ming Lui
Venue: ICCV
Year: 2011

In this paper, we propose novel algorithms for inpainting and refinement of diffeomorphisms. We first represent a diffeomorphism by its Beltrami coefficient. Then it is possible to refine and inpaint the diffeomorphism by processing this Beltrami coefficient. With the inpainted/refined Beltrami coefficient, we construct a new diffeomorphism using the exact Beltrami holomorphic flow algorithm proposed in this paper. We apply our algorithms on several practical applications, which include the inpainting of a highly distorted diffeomorphism, the inpainting of image sequences of deforming shapes, the super-resolution of diffeomorphisms and the global parameterization of cortical surfaces by combining local parameterizations. Experiments show that our algorithm can solve these problems with natural and smooth results. We demonstrate how our proposed method can be widely applied in areas from texture mapping to video processing, and from computer graphics to medical imaging.
Shading-based dynamic shape refinement from multi-view video under general illumination
Authors: Chenglei Wu, Kiran Varanasi, Yebin Liu, Hans-Peter Seidel, Christian Theobalt
Venue: ICCV
Year: 2011

We present an approach to add true fine-scale spatio-temporal shape detail to dynamic scene geometry captured from multi-view video footage. Our approach exploits shading information to recover the millimeter-scale surface structure, but in contrast to related approaches succeeds under general unconstrained lighting conditions. Our method starts off from a set of multi-view video frames and an initial series of reconstructed coarse 3D meshes that lack any surface detail. In a spatio-temporal maximum a posteriori probability (MAP) inference framework, our approach first estimates the incident illumination and the spatially-varying albedo map on the mesh surface for every time instant. Thereafter, albedo and illumination are used to estimate the true geometric detail visible in the images and add it to the coarse reconstructions. The MAP framework uses weak temporal priors on lighting, albedo and geometry which improve reconstruction quality yet allow for temporal variations in the data.
Robust consistent correspondence between 3D non-rigid shapes based on "Dual Shape-DNA"
Authors: Huai-Yu Wu, Hongbin Zha
Venue: ICCV
Year: 2011

In this paper, we propose a novel framework to construct dense and high-quality consistent correspondence between non-rigid surfaces. Our correspondence framework exploits “Dual Shape-DNA” (dual Laplace-Beltrami spectral embedding) to capture global characteristics of objects, and converts two originally different and complex shapes into two similar and simple shapes to facilitate the correspondence. Since our method avoids the computation of geodesic distances, it is robust to local topology changes. By exploiting the excellent properties of the dual domain, our dual spectral framework can robustly construct Laplace-Beltrami embeddings on highly non-regular 3D meshes. After performing initial non-rigid matching in the dual Laplace-Beltrami spectral domain, we return 3D spatial domain and apply a shape-preserving non-rigid deformation to produce the final dense consistent correspondence. We show that our framework is suitable for non-rigid consistent correspondence, and the high-quality correspondence results are achieved.
Low order dynamics embedding for high dimensional time series
Authors: Fei Xiong, Octavia I. Camps, Mario Sznaier
Venue: ICCV
Year: 2011

This paper considers the problem of finding low order nonlinear embeddings of dynamic data, that is, data characterized by a temporal ordering. Examples where this problem arises include, among others, appearance-based multi-frame tracking, activity recognition from video and dynamic texture analysis/synthesis. Our main result is a semi-definite programming based manifold embedding algorithm that exploits the dynamical information encapsulated in the temporal ordering of the sequence to obtain embeddings with lower complexity that those produced by existing algorithms, at a comparable computational cost. In addition, the use of spatio-temporal information allows for minimizing the effects of outliers on the manifold structure and for handling fragmented sequences, where some of the data is missing, for instance due to occlusion.
Dynamic texture classification using dynamic fractal analysis
Authors: Yong Xu 0007, Yuhui Quan, Haibin Ling, Hui Ji 0002
Venue: ICCV
Year: 2011

In this paper, we developed a novel tool called dynamic fractal analysis for dynamic texture (DT) classification, which not only provides a rich description of DT but also has strong robustness to environmental changes. The resulting dynamic fractal spectrum (DFS) for DT sequences consists of two components: One is the volumetric dynamic fractal spectrum component (V-DFS) that captures the stochastic self-similarities of DT sequences as 3D volume datasets; the other is the multi-slice dynamic fractal spectrum component (S-DFS) that encodes fractal structures of DT sequences on 2D slices along different views of the 3D volume. Various types of measures of DT sequences are collected in our approach to analyze DT sequences from different perspectives. The experimental evaluation is conducted on three widely used benchmark datasets. In all the experiments, our method demonstrated excellent performance in comparison with state-of-the-art approaches.
Complementary hashing for approximate nearest neighbor search
Authors: Hao Xu, Jingdong Wang 0001, Zhu Li, Gang Zeng, Shipeng Li 0001, Nenghai Yu
Venue: ICCV
Year: 2011

Recently, hashing based Approximate Nearest Neighbor (ANN) techniques have been attracting lots of attention in computer vision. The data-dependent hashing methods, e.g., Spectral Hashing, expects better performance than the data-blind counterparts, e.g., Locality Sensitive Hashing (LSH). However, most data-dependent hashing methods only employ a single hash table. When higher recall is desired, they have to retrieve exponentially growing number of hash buckets around the bucket containing the query, which may drag down the precision rapidly. In this paper, we propose a so-called complementary hashing approach, which is able to balance the precision and recall in a more effective way. The key idea is to employ multiple complementary hash tables, which are learned sequentially in a boosting manner, so that, given a query, its true nearest neighbors missed from the active bucket of one hash table are more likely to be found in the active bucket of the next hash table. Compared with LSH that also can exploit multiple hash tables, our approach is more effective to find true NNs, thanks to the complementarity property of the hash tables from our approach. Experimental results on large scale ANN search show that the proposed method significantly improves the performance and outperforms the state-of-the-art.
Spatial pyramid co-occurrence for image classification
Authors: Yi Yang 0016, Shawn D. Newsam
Venue: ICCV
Year: 2011

We describe a novel image representation termed spatial pyramid co-occurrence which characterizes both the photometric and geometric aspects of an image. Specifically, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an image. The representation captures both the absolute and relative spatial arrangement of the words and, through the choice and combination of the predicates, can characterize a variety of spatial relationships. Our representation is motivated by the analysis of overhead imagery such as from satellites or aircraft. This imagery generally does not have an absolute reference frame and thus the relative spatial arrangement of the image elements often becomes the key discriminating feature. We validate this hypothesis using a challenging ground truth image dataset of 21 land-use classes manually extracted from high-resolution aerial imagery. Our approach is shown to result in higher classification rates than a non-spatial bagof- visual-words approach as well as a popular approach for characterizing the absolute spatial arrangement of visual words, the spatial pyramid representation of Lazebnik et al. [7]. While our primary objective is analyzing overhead imagery, we demonstrate that our approach achieves state-of-the-art performance on the Graz-01 object class dataset and performs competitively on the 15 Scene dataset.
Accurate 3D pose estimation from a single depth image
Authors: Mao Ye 0005, Xianwang Wang, Ruigang Yang, Liu Ren, Marc Pollefeys
Venue: ICCV
Year: 2011

This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then refined by directly fitting the body configuration with the observation (e.g., the input depth). In addition to the new system architecture, our other contributions include modifying a point cloud smoothing technique to deal with very noisy input depth maps, a point cloud alignment and pose search algorithm that is view-independent and efficient. Experiments on a public dataset show that our approach achieves significantly higher accuracy than previous state-of-art methods.
An adversarial optimization approach to efficient outlier removal
Authors: Jin Yu 0001, Anders P. Eriksson, Tat-Jun Chin, David Suter
Venue: ICCV
Year: 2011

This paper proposes a novel adversarial optimization approach to efficient outlier removal in computer vision. We characterize the outlier removal problem as a game that involves two players of conflicting interests, namely, optimizer and outlier. Such an adversarial view not only brings new insights into various existing methods, but also gives rise to a general optimization framework that provably unifies them. Under the proposed framework, we develop a new outlier removal approach that is able to offer a much needed control over the trade-off between reliability and speed, which is otherwise not available in previous methods. The proposed approach is driven by a mixed-integer minmax (convex-concave) optimization process. Although a minmax problem is generally not amenable to efficient optimization, we show that for some commonly used vision objective functions, an equivalent Linear Program reformulation exists. We demonstrate our method on two representative multiview geometry problems. Experiments on real image data illustrate superior practical performance of our method over recent techniques.
Active scene recognition with vision and language
Authors: Xiaodong Yu 0002, Cornelia Fermüller, Ching Lik Teo, Yezhou Yang, Yiannis Aloimonos
Venue: ICCV
Year: 2011

This paper presents a novel approach to utilizing high level knowledge for the problem of scene recognition in an active vision framework, which we call active scene recognition. In traditional approaches, high level knowledge is used in the post-processing to combine the outputs of the object detectors to achieve better classification performance. In contrast, the proposed approach employs high level knowledge actively by implementing an interaction between a reasoning module and a sensory module (Figure 1). Following this paradigm, we implemented an active scene recognizer and evaluated it with a dataset of 20 scenes and 100+ objects. We also extended it to the analysis of dynamic scenes for activity recognition with attributes. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.
Multiview structure from motion in trajectory space
Authors: Aamer Zaheer, Ijaz Akhter, Mohammad Haris Baig, Shabbir Marzban, Sohaib Khan
Venue: ICCV
Year: 2011

Most nonrigid objects exhibit temporal regularities in their deformations. Recently it was proposed that these regularities can be parameterized by assuming that the nonrigid structure lies in a small dimensional trajectory space. In this paper, we propose a factorization approach for 3D reconstruction from multiple static cameras under the compact trajectory subspace representation. Proposed factorization is analogous to rank-3 factorization of rigid structure from motion problem, in transformed space. The benefit of our approach is that the 3D trajectory basis can be directly learned from the image observations. This also allows us to impute missing observations and denoise tracking errors without explicit estimation of the 3D structure. In contrast to standard triangulation based methods which require points to be visible in at least two cameras, our approach can reconstruct points, which remain occluded even in all the cameras for quite a long time. This makes our solution especially suitable for occlusion handling in motion capture systems. We demonstrate robustness of our method on challenging real and synthetic scenarios.
Structure-sensitive superpixels via geodesic distance
Authors: Gang Zeng, Peng Wang 0001, Jingdong Wang 0001, Rui Gan, Hongbin Zha
Venue: ICCV
Year: 2011

Over-segments (i.e. superpixels) have been commonly used as supporting regions for feature vectors and primitives to reduce computational complexity in various image analysis tasks. In this paper, we describe a structuresensitive over-segmentation technique by exploiting Lloyd's algorithm with a geodesic distance. It generates smaller superpixels to achieve lower under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute the geodesic distances amongst pixels, and in the segmentation procedure, the density of over-segments is automatically adjusted according to an energy functional that embeds color homogeneity, structure density and compactness constraints. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms prior arts while offering a comparable computational efficiency with fast methods, such as TurboPixels.
Simultaneous multi-body stereo and segmentation
Authors: Guofeng Zhang 0001, Jiaya Jia, Hujun Bao
Venue: ICCV
Year: 2011

This paper presents a novel multi-body multi-view stereo method to simultaneously recover dense depth maps and perform segmentation with the input of a monocular image sequence. Unlike traditional multi-view stereo approaches that generally handle a single static scene or an object, we show that depth estimation and segmentation can be jointly modeled and be globally solved in an energy minimization framework for ubiquitous scenes containing multiple independently moving rigid objects. Our major contribution includes a new multi-body stereo model, which integrates the color, geometry, and layer constraints for spatio-temporal depth recovery and automatic object segmentation. A two-pass optimization scheme is proposed to progressively update the estimates. Our method is applied to a variety of challenging examples.
Blurring-invariant Riemannian metrics for comparing signals and images
Authors: Zhengwu Zhang, Eric Klassen, Anuj Srivastava, Pavan K. Turaga, Rama Chellappa
Venue: ICCV
Year: 2011

We propose a novel Riemannian framework for comparing signals and images in a manner that is invariant to their levels of blur. This framework uses a log-Fourier representation of signals/images in which the set of all possible Gaussian blurs of a signal, i.e. its orbits under semigroup action of Gaussian blur functions, is a straight line. Using a set of Riemannian metrics under which the group actions are by isometries, the orbits are compared via distances between orbits. We demonstrate this framework using a number of experimental results involving 1D signals and 2D images.
Unwrapping low-rank textures on generalized cylindrical surfaces
Authors: Zhengdong Zhang 0001, Xiao Liang 0002, Yi Ma 0001
Venue: ICCV
Year: 2011

In this paper, we show how to reconstruct both 3D shape and 2D texture of a class of surfaces from a single perspective image. We consider the so-called the generalized cylindrical surfaces that are wrapped with low-rank textures. They can be used to model most curved building facades in urban areas or deformed book pages scanned for text recognition. Our method leverages on the recent new techniques for low-rank matrix recovery and sparse error correction and it generalizes existing techniques from planar surfaces to a much larger class of important 3D surfaces. As we will show with extensive simulations and experiments, the proposed algorithm can precisely rectify deformation of textures caused by both perspective projection and surface shape. It works for a wide range of symmetric or regular textures that are ubiquitous in images of urban environments, objects, or texts, and it is very robust to sparse occlusion, noise, and saturation.
Multi-observation visual recognition via joint dynamic sparse representation
Authors: Haichao Zhang 0001, Nasser M. Nasrabadi, Yanning Zhang 0001, Thomas S. Huang
Venue: ICCV
Year: 2011

We address the problem of visual recognition from multiple observations of the same physical object, which can be generated under different conditions, such as frames at different time instances or snapshots from different viewpoints. We formulate the multi-observation visual recognition task as a joint sparse representation model and take advantage of the correlations among the multiple observations for classification using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity pattern among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class in order to facilitate a flexible representation. The proposed method can handle both homogenous as well as heterogenous data within the same framework. Extensive experiments on various visual classification tasks including face recognition and generic object classification demonstrate that the proposed method outperforms existing state-of-the-art methods
Close the loop: Joint blind image restoration and recognition with sparse representation prior
Authors: Haichao Zhang 0001, Jianchao Yang, Yanning Zhang 0001, Nasser M. Nasrabadi, Thomas S. Huang
Venue: ICCV
Year: 2011

Most previous visual recognition systems simply assume ideal inputs without real-world degradations, such as low resolution, motion blur and out-of-focus blur. In presence of such unknown degradations, the conventional approach first resorts to blind image restoration and then feeds the restored image into a classifier. Treating restoration and recognition separately, such a straightforward approach, however, suffers greatly from the defective output of the ill-posed blind image restoration. In this paper, we present a joint blind image restoration and recognition method based on the sparse representation prior to handle the challenging problem of face recognition from low-quality images, where the degradation model is realistic and totally unknown. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the training set, which indicates the identity of the test image. The proposed algorithm achieves simultaneous restoration and recognition by iteratively solving the blind image restoration in pursuit of the sparest representation for recognition. Based on such a sparse representation prior, we demonstrate that the image restoration task and the recognition task can benefit greatly from each other. Extensive experiments on face datasets under various degradations are carried out and the results of our joint model shows significant improvements over conventional methods of treating the two tasks independently.
A linear subspace learning approach via sparse coding
Authors: Lei Zhang 0006, Pengfei Zhu 0001, Qinghua Hu, David Zhang 0001
Venue: ICCV
Year: 2011

Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower dimensional space by linear projection. Most LSL methods compute directly the statistics of original training samples to learn the subspace. However, these methods do not effectively exploit the different contributions of different image components to image recognition. We propose a novel LSL approach by sparse coding and feature grouping. A dictionary is learned from the training dataset, and it is used to sparsely decompose the training samples. The decomposed image components are grouped into a more discriminative part (MDP) and a less discriminative part (LDP). An unsupervised criterion and a supervised criterion are then proposed to learn the desired subspace, where the MDP is preserved and the LDP is suppressed simultaneously. The experimental results on benchmark face image databases validated that the proposed methods outperform many state-of-the-art LSL schemes.
Robust unsupervised motion pattern inference from video and applications
Authors: Xuemei Zhao, Gérard G. Medioni
Venue: ICCV
Year: 2011

We propose an unsupervised learning framework to infer motion patterns in videos and in turn use them to improve tracking of moving objects in sequences from static cameras. Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents motion direction. In this space, points automatically form intrinsic manifold structures, each of which corresponds to a motion pattern. To define each group, a novel robustmanifold grouping algorithm is proposed. Tensor Voting is performed to provide multiple geometric cues which formulate multiple similarity kernels between any pair of points, and a spectral clustering technique is used in this multiple kernel setting. The grouping algorithm achieves better performance than state-of-the-art methods in our applications. Extracted motion patterns can then be used as a prior to improve the performance of any object tracker. It is especially useful to reduce false alarms and ID switches. Experiments are performed on challenging real-world sequences, and a quantitative analysis of the results shows the framework effectively improves state-of-the-art tracker.