OpenRaiser/Intern-Atlas huggingface dataset:
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video
Authors: Jun He 0006, Laura Balzano, Arthur Szlam
Venue: CVPR
Year: 2012

It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilize low-dimensional subspaces, demonstrating that subsampling can improve computation speed while still allowing for accurate subspace learning. We present GRASTA, Grassmannian Robust Adaptive Subspace Tracking Algorithm, an online algorithm for robust subspace estimation from randomly subsampled data. We consider the specific application of background and foreground separation in video, and we assess GRASTA on separation accuracy and computation time. In one benchmark video example [16], GRASTA achieves a separation rate of 46.3 frames per second, even when run in MATLAB on a personal laptop.
Computing nearest-neighbor fields via Propagation-Assisted KD-Trees
Authors: Kaiming He, Jian Sun 0001
Venue: CVPR
Year: 2012

Matching patches between two images, also known as computing nearest-neighbor fields, has been proven a useful technique in various computer vision/graphics algorithms. But this is a computationally challenging nearest-neighbor search task, because both the query set and the candidate set are of image size. In this paper, we propose Propagation-Assisted KD-Trees to quickly compute an approximate solution. We develop a novel propagation search method for kd-trees. In this method the tree nodes checked by each query are propagated from the nearby queries. This method not only avoids the time-consuming backtracking in traditional tree methods, but is more accurate. Experiments on public data show that our method is 10-20 times faster than the PatchMatch method [4] at the same accuracy, or reduces its error by 70% at the same running time. Our method is also 2-5 times faster and is more accurate than Coherency Sensitive Hashing [22], a latest state-of-the-art method.
Sparse representation for blind image quality assessment
Authors: Lihuo He, Dacheng Tao, Xuelong Li 0001, Xinbo Gao 0001
Venue: CVPR
Year: 2012

Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: (1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; (2) they are sensitive to different datasets; and (3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics.
Rolling shutter bundle adjustment
Authors: Johan Hedborg, Per-Erik Forssén, Michael Felsberg, Erik Ringaby
Venue: CVPR
Year: 2012

This paper introduces a bundle adjustment (BA) method that obtains accurate structure and motion from rolling shutter (RS) video sequences: RSBA. When a classical BA algorithm processes a rolling shutter video, the resultant camera trajectory is brittle, and complete failures are not uncommon. We exploit the temporal continuity of the camera motion to define residuals of image point trajectories with respect to the camera trajectory. We compare the camera trajectories from RSBA to those from classical BA, and from classical BA on rectified videos. The comparisons are done on real video sequences from an iPhone 4, with ground truth obtained from a global shutter camera, rigidly mounted to the iPhone 4. Compared to classical BA, the rolling shutter model requires just six extra parameters. It also degrades the sparsity of the system Jacobian slightly, but as we demonstrate, the increase in computation time is moderate. Decisive advantages are that RSBA succeeds in cases where competing methods diverge, and consistently produces more accurate results.
A branch-and-bound algorithm for globally optimal hand-eye calibration
Authors: Jan Heller, Michal Havlena, Tomás Pajdla
Venue: CVPR
Year: 2012

This paper introduces a novel solution to hand-eye calibration problem. It is the first method that uses camera measurements directly and at the same time requires neither prior knowledge of the external camera calibrations nor a known calibration device. Our algorithm uses branch-and-bound approach to minimize an objective function based on the epipolar constraint. Further, it employs Linear Programming to decide the bounding step of the algorithm. The presented technique is able to recover both the unknown rotation and translation simultaneously and the solution is guaranteed to be globally optimal with respect to the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm.
Spherical hashing
Authors: Jae-Pil Heo, Youngwoon Lee, Junfeng He, Shih-Fu Chang, Sung-Eui Yoon
Venue: CVPR
Year: 2012

Many binary code encoding schemes based on hashing have been actively studied recently, since they can provide efficient similarity search, especially nearest neighbor search, and compact data representations suitable for handling large scale image databases in many computer vision problems. Existing hashing techniques encode high-dimensional data points by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere-based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared to hyperplane-based hashing functions. Furthermore, we propose a new binary code distance function, spherical Hamming distance, that is tailored to our hypersphere-based binary coding scheme, and design an efficient iterative optimization process to achieve balanced partitioning of data points for each hash function and independence between hashing functions. Our extensive experiments show that our spherical hashing technique significantly outperforms six state-of-the-art hashing techniques based on hyperplanes across various image benchmarks of sizes ranging from one to 75 million of GIST descriptors. The performance gains are consistent and large, up to 100% improvements. The excellent results confirm the unique merits of the proposed idea in using hyperspheres to encode proximity regions in high-dimensional spaces. Finally, our method is intuitive and easy to implement.
Occlusion reasoning for object detection under arbitrary viewpoint
Authors: Edward Hsiao, Martial Hebert
Venue: CVPR
Year: 2012

We present a unified occlusion model for object instance detection under arbitrary viewpoint. Whereas previous approaches primarily modeled local coherency of occlusions or attempted to learn the structure of occlusions from data, we propose to explicitly model occlusions by reasoning about 3D interactions of objects. Our approach accurately represents occlusions under arbitrary viewpoint without requiring additional training data, which can often be difficult to obtain. We validate our model by extending the state-of-the-art LINE2D method for object instance detection and demonstrate significant improvement in recognizing textureless objects under severe occlusions.
Learning 3D object templates by hierarchical quantization of geometry and appearance spaces
Authors: Wenze Hu
Venue: CVPR
Year: 2012

This paper presents a method for learning 3D object templates from view labeled object images. The 3D template is defined in a joint appearance and geometry space composed of deformable planar part templates placed at different 3D positions and orientations. Appearance of each part template is represented by Gabor filters, which are hierarchically grouped into line segments and geometric shapes. AND-OR trees are further used to quantize the possible geometry and appearance of part templates, so that learning can be done on a subsampled discrete space. Using information gain as a criterion, the best 3D template can be searched through the AND-OR trees using one bottom-up pass and one top-down pass. Experiments on a new car dataset with diverse views show that the proposed method can learn meaningful 3D car templates, and give satisfactory detection and view estimation performance. Experiments are also performed on a public car dataset, which show comparable performance with recent methods.
A fast nearest neighbor search algorithm by nonlinear embedding
Authors: Yoonho Hwang, Bohyung Han, Hee-Kap Ahn
Venue: CVPR
Year: 2012

We propose an efficient algorithm to find the exact nearest neighbor based on the Euclidean distance for large-scale computer vision problems. We embed data points nonlinearly onto a low-dimensional space by simple computations and prove that the distance between two points in the embedded space is bounded by the distance in the original space. Instead of computing the distances in the high-dimensional original space to find the nearest neighbor, a lot of candidates are to be rejected based on the distances in the low-dimensional embedded space; due to this property, our algorithm is well-suited for high-dimensional and large-scale problems. We also show that our algorithm is improved further by partitioning input vectors recursively. Contrary to most of existing fast nearest neighbor search algorithms, our technique reports the exact nearest neighbor - not an approximate one - and requires a very simple preprocessing with no sophisticated data structures. We provide the theoretical analysis of our algorithm and evaluate its performance in synthetic and real data.
Robust photometric stereo using sparse regression
Authors: Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa
Venue: CVPR
Year: 2012

This paper presents a robust photometric stereo method that effectively compensates for various non-Lambertian corruptions such as specularities, shadows, and image noise. We construct a constrained sparse regression problem that enforces both Lambertian, rank-3 structure and sparse, additive corruptions. A solution method is derived using a hierarchical Bayesian approximation to accurately estimate the surface normals while simultaneously separating the non-Lambertian corruptions. Extensive evaluations are performed that show state-of-the-art performance using both synthetic and real-world images.
Regression Tree Fields - An efficient, non-parametric approach to image labeling problems
Authors: Jeremy Jancsary, Sebastian Nowozin, Toby Sharp, Carsten Rother
Venue: CVPR
Year: 2012

We introduce Regression Tree Fields (RTFs), a fully conditional random field model for image labeling problems. RTFs gain their expressive power from the use of non-parametric regression trees that specify a tractable Gaussian random field, thereby ensuring globally consistent predictions. Our approach improves on the recently introduced decision tree field (DTF) model [14] in three key ways: (i) RTFs have tractable test-time inference, making efficient optimal predictions feasible and orders of magnitude faster than for DTFs, (ii) RTFs can be applied to both discrete and continuous vector-valued labeling tasks, and (Hi) the entire model, including the structure of the regression trees and energy function parameters, can be efficiently and jointly learned from training data. We demonstrate the expressive power and flexibility of the RTF model on a wide variety of tasks, including inpainting, colorization, denoising, and joint detection and registration. We achieve excellent predictive performance which is on par with, or even surpassing, DTFs on all tasks where a comparison is possible.
Towards compact topical descriptors
Authors: Rongrong Ji, Ling-Yu Duan, Jie Chen 0006, Wen Gao 0001
Venue: CVPR
Year: 2012

We introduce a Compact Topical Descriptor to learn a compact yet discriminative image signature from the reference image corpus. This descriptor is deployed over the well used bag-of-words image histogram, with two merits over the traditional topical features: First, we propose to directly control the topical sparsity to achieve the descriptor compactness. Second, we ensure the descriptor discriminability by minimizing the bag-of-words reconstruction errors during the topical histogram encoding. To this end, we have a generative viewpoint of the topical feature extraction, which is estimated as a sparse MAP estimation over the original bag-of-words. We learn such estimation by a bi-convex optimization, iterating between both hierarchical sparse coding from words to topical histograms and dictionary learning of the corresponding word-to-topic transform. Especially, supervised labels such as image ranking list can be also incorporated into our descriptor learning paradigm. We quantize our performance in both Im-ageNet 10K and NUS-WIDE, with comparisons to bag-of-words, LDA, miniBoF, and Aggregated Local Descriptors. In practice, we also implement our descriptor for a low bit rate mobile visual search application, i.e. sending compact descriptors instead of the image to reduce the query delivery latency. Our descriptor has significantly outperformed the state-of-the-art compact descriptors by quantitative evaluations over 10 million reference images.
A two-stage approach to blind spatially-varying motion deblurring
Authors: Hui Ji 0002, Kang Wang
Venue: CVPR
Year: 2012

Many blind motion deblur methods model the motion blur as a spatially invariant convolution process. However, motion blur caused by the camera movement in 3D space during shutter time often leads to spatially varying blurring effect over the image. In this paper, we proposed an efficient two-stage approach to remove spatially-varying motion blurring from a single photo. There are three main components in our approach: (i) a minimization method of estimating region-wise blur kernels by using both image information and correlations among neighboring kernels, (ii) an interpolation scheme of constructing pixel-wise blur matrix from region-wise blur kernels, and (iii) a non-blind deblurring method robust to kernel errors. The experiments showed that the proposed method outperformed the existing software based approaches on tested real images.
Linear solution to scale invariant global figure ground separation
Authors: Hao Jiang 0007
Venue: CVPR
Year: 2012

We propose a novel linear method for scale invariant figure ground separation in images and videos. Figure ground separation is treated as a superpixel labeling problem. We optimize superpixel foreground and background labeling so that the object foreground estimation matches model color histogram, its area and perimeter are consistent with object shape prior, and the foreground superpixels form a connected region. This optimization problem is challenging due to high-order soft and hard global constraints among large number of superpixels. We devise a scale invariant linear method that gives an integer solution with a guaranteed error bound via a branch and cut procedure. The proposed method does not rely on motion continuity and works on static images and videos with abrupt motion. Our experimental results on both synthetic ground truth data and real images show that the proposed method is efficient and robust over object appearance changes, large deformation and strong background clutter.
Order determination and sparsity-regularized metric learning adaptive visual tracking
Authors: Nan Jiang 0016, Wenyu Liu 0001, Ying Wu 0001
Venue: CVPR
Year: 2012

Recent attempts of integrating metric learning in visual tracking have produced encouraging results. Instead of using fixed and pre-specified metric in visual appearance matching, these methods are able to learn and adjust the metric adaptively by finding the best projection of the feature space. Such learned metric is by design the best to discriminate the target of interest and its distracters from the background. However, an important issue remained unaddressed is how we can determine the optimal dimensionality of the projection to achieve best discrimination. Using inappropriate dimensions for the projection is likely to result in larger classification error, or higher computational costs and over-fitting. This paper presents a novel solution to this structural order determination problem, by introducing sparsity regularization for metric learning (or SRML). This regularization leads to the lowest possible dimensionality of the projection and thus determining the best order. This can actually be viewed as the minimum description length regularization in metric learning. The experiments validate this new approach on standard benchmark datasets, and demonstrate its effectiveness in visual tracking applications.
Scale resilient, rotation invariant articulated object matching
Authors: Hao Jiang 0007, Tai-Peng Tian, Kun He 0003, Stan Sclaroff
Venue: CVPR
Year: 2012

A novel method is proposed for matching articulated objects in cluttered videos. The method needs only a single exemplar image of the target object. Instead of using a small set of large parts to represent an articulated object, the proposed model uses hundreds of small units to represent walks along paths of pixels between key points on an articulated object. Matching directly on dense pixels is key to achieving reliable matching when motion blur occurs. The proposed method fits the model to local image properties, conforms to structure constraints, and remembers the steps taken along a pixel path. The model formulation handles variations in object scaling, rotation and articulation. Recovery of the optimal pixel walks is posed as a special shortest path problem, which can be solved efficiently via dynamic programming. Further speedup is achieved via factorization of the path costs. An efficient method is proposed to find multiple walks and simultaneously match multiple key points. Experiments show that the proposed method is efficient and reliable and can be used to match articulated objects in fast motion videos with strong clutter and blurry imagery.
Submodular dictionary learning for sparse coding
Authors: Zhuolin Jiang, Guangxiao Zhang, Larry S. Davis
Venue: CVPR
Year: 2012

A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: entropy rate of a random walk on a graph and a discriminative term. Dictionary learning is achieved by finding a graph topology which maximizes the objective function. By exploiting the monotonicity and submodularity properties of the objective function and the matroid constraint, we present a highly efficient greedy-based optimization algorithm. It is more than an order of magnitude faster than several recently proposed dictionary learning approaches. Moreover, the greedy algorithm gives a near-optimal solution with a (1/2)-approximation bound. Our approach yields dictionaries having the property that feature points from the same class have very similar sparse codes. Experimental results demonstrate that our approach outperforms several recently proposed dictionary learning techniques for face, action and object category recognition.
Modelling and correction of multipath interference in time of flight cameras
Authors: David Fuentes-Jiménez, Daniel Pizarro 0001, Manuel Mazo 0001, Sira E. Palazuelos
Venue: CVPR
Year: 2012

This paper presents an algorithm that automatically corrects the distortion caused by multipath interference (MpI) in depth measurements obtained with time of flight cameras (ToF cameras). A radiometric model that explains, under some mild simplifications, the working principle of a ToF camera including a model for MpI is proposed. Using this model we demonstrate that all the information needed for compensating the influence of MpI on the scene captured by the camera is self-contained in the measurements (depth and amplitude of infrared signal). We propose an iterative optimization method that, based on the measurements contaminated with MpI, gives depth correction for each pixel. Results are shown in artificially generated time of flight scenes using the radiometric model. In addition, the system has been validated in real scenes using a commercial ToF camera providing good results.
Small sample scene categorization from perceptual relations
Authors: Ilan Kadar, Ohad Ben-Shahar
Venue: CVPR
Year: 2012

This paper addresses the problem of scene categorization while arguing that better and more accurate results can be obtained by endowing the computational process with perceptual relations between scene categories. We first describe a psychophysical paradigm that probes human scene categorization, extracts perceptual relations between scene categories, and suggests that these perceptual relations do not always conform the semantic structure between categories. We then incorporate the obtained perceptual findings into a computational classification scheme, which takes inter-class relationships into account to obtain better scene categorization regardless of the particular descriptors with which scenes are represented. We present such improved classification results using several popular descriptors, we discuss why the contribution of inter-class perceptual relations is particularly pronounced for under-sampled training sets, and we argue that this mechanism may explain the ability of the human visual system to perform well under similar conditions. Finally, we introduce an online experimental system for obtaining perceptual relations for large collections of scene categories.
Online incremental attribute-based zero-shot learning
Authors: Pichai Kankuekul, Aram Kawewong, Sirinart Tangruamsub, Osamu Hasegawa
Venue: CVPR
Year: 2012

The paper presents a new online incremental zero-shot learning method for applications in robotics and mobile communications where attribute labeling is obtained via online interaction with users, and where the potential for inconsistency exists. Unique to most previous offline batch learning methods, the proposed method is based on the indirect-attribute-prediction (IAP) model instead of the direct-attribute-prediction (DAP). Using self-organizing and incremental neural networks (SOINN) as the learning mechanism, our method can learn new attributes and update existing attributes in an online incremental manner while retaining as high accuracy as that of the state-of-the-art offline method. Compared to the offline methods, the computation time has also been reduced by more than 99%. Two experiments evaluated two aspects of the proposed method. First, our method clearly outperforms the previous IAP-based offline method in terms of both time and accuracy, and yield approximately the same accuracy as the DAP-based offline method. Second, the proposed method can deal with situations where object attributes are gradually labeled via interaction with many users and where some of them may be incorrect. This scenario is very important for applications in mobile communications and robotics where some objects and attributes may be initially unknown and must be learnt online.