pwc-archive/papers-with-abstracts huggingface dataset:
A Max-Margin Riffled Independence Model for Image Tag Ranking
Authors: Tian Lan, Greg Mori
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Attribute, Retrieval, TAG

We propose Max-Margin Riffled Independence Model (MMRIM), a new method for image tag ranking modeling the structured preferences among tags. The goal is to predict a ranked tag list for a given image, where tags are ordered by their importance or relevance to the image content. Our model integrates the max-margin formalism with riffled independence factorizations proposed in [10], which naturally allows for structured learning and efficient ranking. Experimental results on the SUN Attribute and LabelMe datasets demonstrate the superior performance of the proposed model compared with baseline tag ranking methods. We also apply the predicted rank list of tags to several higher-level computer vision applications in image understanding and retrieval, and demonstrate that MMRIM significantly improves the accuracy of these applications.
Label Propagation from ImageNet to 3D Point Clouds
Authors: Yan Wang, Rongrong Ji, Shih-Fu Chang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Recent years have witnessed a growing interest in understanding the semantics of point clouds in a wide variety of applications. However, point cloud labeling remains an open problem, due to the difficulty in acquiring sufficient 3D point labels towards training effective classifiers. In this paper, we overcome this challenge by utilizing the existing massive 2D semantic labeled datasets from decadelong community efforts, such as ImageNet and LabelMe, and a novel "cross-domain" label propagation approach. Our proposed method consists of two major novel components, Exemplar SVM based label propagation, which effectively addresses the cross-domain issue, and a graphical model based contextual refinement incorporating 3D constraints. Most importantly, the entire process does not require any training data from the target scenes, also with good scalability towards large scale applications. We evaluate our approach on the well-known Cornell Point Cloud Dataset, achieving much greater efficiency and comparable accuracy even without any 3D training data. Our approach shows further major gains in accuracy when the training data from the target scenes is used, outperforming state-ofthe-art approaches with far better efficiency.
Supervised Semantic Gradient Extraction Using Linear-Time Optimization
Authors: Shulin Yang, Jue Wang, Linda Shapiro
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Edge Detection

This paper proposes a new supervised semantic edge and gradient extraction approach, which allows the user to roughly scribble over the desired region to extract semantically-dominant and coherent edges in it. Our approach first extracts low-level edgelets (small edge clusters) from the input image as primitives and build a graph upon them, by jointly considering both the geometric and appearance compatibility of edgelets. Given the characteristics of the graph, it cannot be effectively optimized by commonly-used energy minimization tools such as graph cuts. We thus propose an efficient linear algorithm for precise graph optimization, by taking advantage of the special structure of the graph. Objective evaluations show that the proposed method significantly outperforms previous semantic edge detection algorithms. Finally, we demonstrate the effectiveness of the system in various image editing tasks.
Deep Learning Shape Priors for Object Segmentation
Authors: Fei Chen, Huimin Yu, Roland Hu, Xunxun Zeng
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Deep Learning, Object, Semantic Segmentation

In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used to model shape variations of global and local structures in an energetic form. Finally, it is applied to data-driven variational methods to perform object extraction of corrupted data based on shape probabilistic representation. Experiments demonstrate that our model can be applied to dataset of arbitrary prior shapes, and can cope with image noise and clutter, as well as partial occlusions.
Consensus of k-NNs for Robust Neighborhood Selection on Graph-Based Manifolds
Authors: Vittal Premachandran, Ramakrishna Kakarala
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Propagating similarity information along the data manifold requires careful selection of local neighborhood. Selecting a "good" neighborhood in an unsupervised setting, given an affinity graph, has been a difficult task. The most common way to select a local neighborhood has been to use the k-nearest neighborhood (k-NN) selection criterion. However, it has the tendency to include noisy edges. In this paper, we propose a way to select a robust neighborhood using the consensus of multiple rounds of k-NNs. We explain how using consensus information can give better control over neighborhood selection. We also explain in detail the problems with another recently proposed neighborhood selection criteria, i.e., Dominant Neighbors, and show that our method is immune to those problems. Finally, we show the results from experiments in which we compare our method to other neighborhood selection approaches. The results corroborate our claims that consensus of k-NNs does indeed help in selecting more robust and stable localities.
Semi-supervised Learning with Constraints for Person Identification in Multimedia Data
Authors: Martin Bauml, Makarand Tapaswi, Rainer Stiefelhagen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Face Recognition, General Classification, Person Identification, regression

We address the problem of person identification in TV series. We propose a unified learning framework for multiclass classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. The method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.
Capturing Layers in Image Collections with Componential Models: From the Layered Epitome to the Componential Counting Grid
Authors: Alessandro Perina, Nebojsa Jojic
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Recently, the Counting Grid (CG) model [5] was developed to represent each input image as a point in a large grid of feature counts. This latent point is a corner of a window of grid points which are all uniformly combined to match the (normalized) feature counts in the image. Being a bag of word model with spatial layout in the latent space, the CG model has superior handling of field of view changes in comparison to other bag of word models, but with the price of being essentially a mixture, mapping each scene to a single window in the grid. In this paper we introduce a family of componential models, dubbed the Componential Counting Grid, whose members represent each input image by multiple latent locations, rather than just one. In this way, we make a substantially more flexible admixture model which captures layers or parts of images and maps them to separate windows in a Counting Grid. We tested the models on scene and place classification where their componential nature helped to extract objects, to capture parallax effects, thus better fitting the data and outperforming Counting Grids and Latent Dirichlet Allocation, especially on sequences taken with wearable cameras.
Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras
Authors: Ju Shen, Sen-Ching S. Cheung
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Denoising

The recent popularity of structured-light depth sensors has enabled many new applications from gesture-based user interface to 3D reconstructions. The quality of the depth measurements of these systems, however, is far from perfect. Some depth values can have significant errors, while others can be missing altogether. The uncertainty in depth measurements among these sensors can significantly degrade the performance of any subsequent vision processing. In this paper, we propose a novel probabilistic model to capture various types of uncertainties in the depth measurement process among structured-light systems. The key to our model is the use of depth layers to account for the differences between foreground objects and background scene, the missing depth value phenomenon, and the correlation between color and depth channels. The depth layer labeling is solved as a maximum a-posteriori estimation problem, and a Markov Random Field attuned to the uncertainty in measurements is used to spatially smooth the labeling process. Using the depth-layer labels, we propose a depth correction and completion algorithm that outperforms other techniques in the literature.
Adaptive Compressed Tomography Sensing
Authors: Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Computed Tomography (CT)

One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image. We propose a mathematical model for adaptive CT acquisition whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited acquisition and improved reconstruction, with the goal of applying only the dose level required for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation and a discrete form of Ridgelet analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where for the same number of line projections, the adaptive model produces higher image quality, when compared with standard limited angle, non-adaptive acquisition algorithms.
Detection of Manipulation Action Consequences (MAC)
Authors: Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Recognition, Temporal Action Localization

The problem of action recognition and human activity has been an active research area in Computer Vision and Robotics. While full-body motions can be characterized by movement and change of posture, no characterization, that holds invariance, has yet been proposed for the description of manipulation actions. We propose that a fundamental concept in understanding such actions, are the consequences of actions. There is a small set of fundamental primitive action consequences that provides a systematic high-level classification of manipulation actions. In this paper a technique is developed to recognize these action consequences. At the heart of the technique lies a novel active tracking and segmentation method that monitors the changes in appearance and topological structure of the manipulated object. These are then used in a visual semantic graph (VSG) based procedure applied to the time sequence of the monitored object to recognize the action consequence. We provide a new dataset, called Manipulation Action Consequences (MAC 1.0), which can serve as testbed for other studies on this topic. Several experiments on this dataset demonstrates that our method can robustly track objects and detect their deformations and division during the manipulation. Quantitative tests prove the effectiveness and efficiency of the method.
Efficient Color Boundary Detection with Color-Opponent Mechanisms
Authors: Kaifu Yang, Shao-Bing Gao, Chaoyi Li, Yong-Jie Li
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Boundary Detection

Color information plays an important role in better understanding of natural scenes by at least facilitating discriminating boundaries of objects or areas. In this study, we propose a new framework for boundary detection in complex natural scenes based on the color-opponent mechanisms of the visual system. The red-green and blue-yellow color opponent channels in the human visual system are regarded as the building blocks for various color perception tasks such as boundary detection. The proposed framework is a feedforward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to the primary visual cortex (V1). Results show that our simple framework has excellent ability to flexibly capture both the structured chromatic and achromatic boundaries in complex scenes.
Better Exploiting Motion for Better Action Recognition
Authors: Mihir Jain, Herve Jegou, Patrick Bouthemy
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Recognition, Image Retrieval, Retrieval, Temporal Action Localization, Video Description

Several recent works on action recognition have attested the importance of explicitly integrating motion characteristics in the video description. This paper establishes that adequately decomposing visual motion into dominant and residual motions, both in the extraction of the space-time trajectories and for the computation of descriptors, significantly improves action recognition algorithms. Then, we design a new motion descriptor, the DCS descriptor, based on differential motion scalar quantities, divergence, curl and shear features. It captures additional information on the local motion patterns enhancing results. Finally, applying the recent VLAD coding technique proposed in image retrieval provides a substantial improvement for action recognition. Our three contributions are complementary and lead to outperform all reported results by a significant margin on three challenging datasets, namely Hollywood 2, HMDB51 and Olympic Sports.
Constraints as Features
Authors: Shmuel Asafi, Daniel Cohen-Or
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, Constrained Clustering, Image Segmentation, Semantic Segmentation

In this paper, we introduce a new approach to constrained clustering which treats the constraints as features. Our method augments the original feature space with additional dimensions, each of which derived from a given Cannot-link constraints. The specified Cannot-link pair gets extreme coordinates values, and the rest of the points get coordinate values that express their spatial influence from the specified constrained pair. After augmenting all the new features, a standard unconstrained clustering algorithm can be performed, like k-means or spectral clustering. We demonstrate the efficacy of our method for active semi-supervised learning applied to image segmentation and compare it to alternative methods. We also evaluate the performance of our method on the four most commonly evaluated datasets from the UCI machine learning repository.
Graph-Laplacian PCA: Closed-Form Solution and Robustness
Authors: Bo Jiang, Chris Ding, Bio Luo, Jin Tang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, Form, General Classification, Image Reconstruction

Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation. In many applications, both vector data X and graph data W are available. Laplacian embedding is widely used for embedding graph data. We propose a graph-Laplacian PCA (gLPCA) to learn a low dimensional representation of X that incorporates graph structures encoded in W . This model has several advantages: (1) It is a data representation model. (2) It has a compact closed-form solution and can be efficiently computed. (3) It is capable to remove corruptions. Extensive experiments on 8 datasets show promising results on image reconstruction and significant improvement on clustering and classification.
Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform
Authors: Tak-Wai Hui, Ronald Chung
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Motion Estimation, Translation

We address the problem of recovering camera motion from video data, which does not require the establishment of feature correspondences or computation of optical flows but from normal flows directly. We have designed an imaging system that has a wide field of view by fixating a number of cameras together to form an approximate spherical eye. With a substantially widened visual field, we discover that estimating the directions of translation and rotation components of the motion separately are possible and particularly efficient. In addition, the inherent ambiguities between translation and rotation also disappear. Magnitude of rotation is recovered subsequently. Experimental results on synthetic and real image data are provided. The results show that not only the accuracy of motion estimation is comparable to those of the state-of-the-art methods that require explicit feature correspondences or optical flows, but also a faster computation time.
Visual Place Recognition with Repetitive Structures
Authors: Akihiko Torii, Josef Sivic, Tomas Pajdla, Masatoshi Okutomi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Retrieval, Visual Place Recognition

Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition. Repeated structures are notoriously hard for establishing correspondences using multi-view geometry. Even more importantly, they violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance. In this work we show that repeated structures are not a nuisance but, when appropriately represented, they form an important distinguishing feature for many places. We describe a representation of repeated structures suitable for scalable retrieval. It is based on robust detection of repeated image structures and a simple modification of weights in the bag-of-visual-word model. Place recognition results are shown on datasets of street-level imagery from Pittsburgh and San Francisco demonstrating significant gains in recognition performance compared to the standard bag-of-visual-words baseline and more recently proposed burstiness weighting.
Single-Pedestrian Detection Aided by Multi-pedestrian Detection
Authors: Wanli Ouyang, Xiaogang Wang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Pedestrian Detection

In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by singleand multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public datasets: Caltech, TUD-Brussels and ETH. Experimental results show that our framework significantly improves all these approaches. The average improvement is 9% on the Caltech-Test dataset, aveaon the TUD-Brussels dataset and teh-on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from 48% to 43% on the Caltech-Test dataset, from edueto fom4on the TUD-Brussels dataset and from etfoto 55%ton the ETH dataset.
Understanding Bayesian Rooms Using Composite 3D Object Models
Authors: Luca Del Pero, Joshua Bowdish, Bonnie Kermgard, Emily Hartley, Kobus Barnard
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, Object Recognition, Room Layout Estimation, Scene Understanding

We develop a comprehensive Bayesian generative model for understanding indoor scenes. While it is common in this domain to approximate objects with 3D bounding boxes, we propose using strong representations with finer granularity. For example, we model a chair as a set of four legs, a seat and a backrest. We find that modeling detailed geometry improves recognition and reconstruction, and enables more refined use of appearance for scene understanding. We demonstrate this with a new likelihood function that rewards 3D object hypotheses whose 2D projection is more uniform in color distribution. Such a measure would be confused by background pixels if we used a bounding box to represent a concave object like a chair. Complex objects are modeled using a set or re-usable 3D parts, and we show that this representation captures much of the variation among object instances with relatively few parameters. We also designed specific data-driven inference mechanisms for each part that are shared by all objects containing that part, which helps make inference transparent to the modeler. Further, we show how to exploit contextual relationships to detect more objects, by, for example,proposing chairs around and underneath tables. We present results showing the benefits of each of these innovations. The performance of our approach often exceeds that of state-of-the-art methods on the two tasks of room layout estimation and object recognition, as evaluated on two bench mark data sets used in this domain.
Groupwise Registration via Graph Shrinkage on the Image Manifold
Authors: Shihui Ying, Guorong Wu, Qian Wang, Dinggang Shen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: All, Image Registration

Recently, groupwise registration has been investigated for simultaneous alignment of all images without selecting any individual image as the template, thus avoiding the potential bias in image registration. However, none of current groupwise registration method fully utilizes the image distribution to guide the registration. Thus, the registration performance usually suffers from large inter-subject variations across individual images. To solve this issue, we propose a novel groupwise registration algorithm for large population dataset, guided by the image distribution on the manifold. Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both synthetic and real datasets, with comparison to the two state-of-the-art groupwise registration methods. All experimental results show that our proposed method achieves the best performance in terms of registration accuracy and robustness.