pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network
Authors: Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivitybased biomarkers for the Alzheimer's disease (AD). In this regard, brain "effective connectivity" analysis, which studies the causal relationship among brain regions, is highly challenging and of many research opportunities. Most of the existing works in this field use generative methods. Despite their success in data representation and other important merits, generative methods are not necessarily discriminative, which may cause the ignorance of subtle but critical disease-induced changes. In this paper, we propose a learning-based approach that integrates the benefits of generative and discriminative methods to recover effective connectivity. In particular, we employ Fisher kernel to bridge the generative models of sparse Bayesian networks (SBN) and the discriminative classifiers of SVMs, and convert the SBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. Our method is able to simultaneously boost the discriminative power of both the generative SBN models and the SBN-induced SVM classifiers via Fisher kernel. The proposed method is tested on analyzing brain effective connectivity for AD from ADNI data, and demonstrates significant improvements over the state-of-the-art work.
Robust Monocular Epipolar Flow Estimation
Authors: Koichiro Yamaguchi, David Mcallester, Raquel Urtasun
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Optical Flow Estimation

We consider the problem of computing optical flow in monocular video taken from a moving vehicle. In this setting, the vast majority of image flow is due to the vehicle's ego-motion. We propose to take advantage of this fact and estimate flow along the epipolar lines of the egomotion. Towards this goal, we derive a slanted-plane MRF model which explicitly reasons about the ordering of planes and their physical validity at junctions. Furthermore, we present a bottom-up grouping algorithm which produces over-segmentations that respect flow boundaries. We demonstrate the effectiveness of our approach in the challenging KITTI flow benchmark [11] achieving half the error of the best competing general flow algorithm and one third of the error of the best epipolar flow algorithm.
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine
Authors: Hua Wang, Feiping Nie, Heng Huang, Chris Ding
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Feature Importance, image-classification, Image Classification, Multi-Label Image Classification, Object Categorization, Scene Understanding

To better understand, search, and classify image and video information, many visual feature descriptors have been proposed to describe elementary visual characteristics, such as the shape, the color, the texture, etc. How to integrate these heterogeneous visual features and identify the important ones from them for specific vision tasks has become an increasingly critical problem. In this paper, We propose a novel Sparse Multimodal Learning (SMML) approach to integrate such heterogeneous features by using the joint structured sparsity regularizations to learn the feature importance of for the vision tasks from both group-wise and individual point of views. A new optimization algorithm is also introduced to solve the non-smooth objective with rigorously proved global convergence. We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks. For each data set we integrate six different types of popularly used image features. Compared to existing scene and object categorization methods using either single modality or multimodalities of features, our approach always achieves better performances measured.
A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching
Authors: Pradipto Das, Chenliang Xu, Richard F. Doell, Jason J. Corso
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Description, Video Description

The problem of describing images through natural language has gained importance in the computer vision community. Solutions to image description have either focused on a top-down approach of generating language through combinations of object detections and language models or bottom-up propagation of keyword tags from training images to test images through probabilistic or nearest neighbor techniques. In contrast, describing videos with natural language is a less studied problem. In this paper, we combine ideas from the bottom-up and top-down approaches to image description and propose a method for video description that captures the most relevant contents of a video in a natural language description. We propose a hybrid system consisting of a low level multimodal latent topic model for initial keyword annotation, a middle level of concept detectors and a high level module to produce final lingual descriptions. We compare the results of our system to human descriptions in both short and long forms on two datasets, and demonstrate that final system output has greater agreement with the human descriptions than any single level.
Online Dominant and Anomalous Behavior Detection in Videos
Authors: Mehrsan Javan Roshtkhari, Martin D. Levine
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
Learning Class-to-Image Distance with Object Matchings
Authors: Guang-Tong Zhou, Tian Lan, Weilong Yang, Greg Mori
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Classification, General Classification, image-classification, Image Classification, Object, Scene Classification

We conduct image classification by learning a class-toimage distance function that matches objects. The set of objects in training images for an image class are treated as a collage. When presented with a test image, the best matching between this collage of training image objects and those in the test image is found. We validate the efficacy of the proposed model on the PASCAL 07 and SUN 09 datasets, showing that our model is effective for object classification and scene classification tasks. State-of-the-art image classification results are obtained, and qualitative results demonstrate that objects can be accurately matched.
Spectral Modeling and Relighting of Reflective-Fluorescent Scenes
Authors: Antony Lam, Imari Sato
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Hyperspectral reflectance data allows for highly accurate spectral relighting under arbitrary illumination, which is invaluable to applications ranging from archiving cultural e-heritage to consumer product design. Past methods for capturing the spectral reflectance of scenes has proven successful in relighting but they all share a common assumption. All the methods do not consider the effects of fluorescence despite fluorescence being found in many everyday objects. In this paper, we describe the very different ways that reflectance and fluorescence interact with illuminants and show the need to explicitly consider fluorescence in the relighting problem. We then propose a robust method based on well established theories of reflectance and fluorescence for imaging each of these components. Finally, we show that we can relight real scenes of reflective-fluorescent surfaces with much higher accuracy in comparison to only considering the reflective component.
Is There a Procedural Logic to Architecture?
Authors: Julien Weissenberg, Hayko Riemenschneider, Mukta Prasad, Luc van Gool
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, Retrieval

Urban models are key to navigation, architecture and entertainment. Apart from visualizing fac,ades, a number of tedious tasks remain largely manual (e.g. compression, generating new fac,ade designs and structurally comparing fac,ades for classification, retrieval and clustering). We propose a novel procedural modelling method to automatically learn a grammar from a set of fac,ades, generate new fac,ade instances and compare fac,ades. To deal with the difficulty of grammatical inference, we reformulate the problem. Instead of inferring a compromising, onesize-fits-all, single grammar for all tasks, we infer a model whose successive refinements are production rules tailored for each task. We demonstrate our automatic rule inference on datasets of two different architectural styles. Our method supercedes manual expert work and cuts the time required to build a procedural model of a fac,ade from several days to a few milliseconds.
Motion Estimation for Self-Driving Cars with a Generalized Camera
Authors: Gim Hee Lee, Friedrich Faundorfer, Marc Pollefeys
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Motion Estimation, Self-Driving Cars

In this paper, we present a visual ego-motion estimation algorithm for a self-driving car equipped with a closeto-market multi-camera system. By modeling the multicamera system as a generalized camera and applying the non-holonomic motion constraint of a car, we show that this leads to a novel 2-point minimal solution for the generalized essential matrix where the full relative motion including metric scale can be obtained. We provide the analytical solutions for the general case with at least one inter-camera correspondence and a special case with only intra-camera correspondences. We show that up to a maximum of 6 solutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the special case with only intra-camera correspondences where the scale becomes unobservable and provide a practical alternative solution. Our formulation can be efficiently implemented within RANSAC for robust estimation. We verify the validity of our assumptions on the motion model by comparing our results on a large real-world dataset collected by a car equipped with 4 cameras with minimal overlapping field-of-views against the GPS/INS ground truth.
Histograms of Sparse Codes for Object Detection
Authors: Xiaofeng Ren, Deva Ramanan
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Dimensionality Reduction, Object, object-detection, Object Detection

Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features. Can we go beyond gradients and do better than HOG? We provide an affirmative answer by proposing and investigating a sparse representation for object detection, Histograms of Sparse Codes (HSC). We compute sparse codes with dictionaries learned from data using K-SVD, and aggregate per-pixel sparse codes to form local histograms. We intentionally keep true to the sliding window framework (with mixtures and parts) and only change the underlying features. To keep training (and testing) efficient, we apply dimension reduction by computing SVD on learned models, and adopt supervised training where latent positions of roots and parts are given externally e.g. from a HOG-based detector. By learning and using local representations that are much more expressive than gradients, we demonstrate large improvements over the state of the art on the PASCAL benchmark for both rootonly and part-based models.
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions
Authors: Dong Zhang, Omar Javed, Mubarak Shah
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, Optical Flow Estimation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

In this paper, we propose a novel approach to extract primary object segments in videos in the 'object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.
Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition
Authors: Ziheng Wang, Shangfei Wang, Qiang Ji
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited. One contributing factor for this is the limited ability of the current dynamic models in capturing complex spatial and temporal relations. Existing dynamic models can only capture simple local temporal relations among sequential events, or lack the ability for incorporating uncertainties. To overcome these limitations and take full advantage of the spatio-temporal information, we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events. We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition. Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles, as well as its advantage over the existing methods.
Bayesian Depth-from-Defocus with Shading Constraints
Authors: Chen Li, Shuochen Su, Yasuyuki Matsushita, Kun Zhou, Stephen Lin
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Depth Estimation

We present a method that enhances the performance of depth-from-defocus (DFD) through the use of shading information. DFD suffers from important limitations namely coarse shape reconstruction and poor accuracy on textureless surfaces that can be overcome with the help of shading. We integrate both forms of data within a Bayesian framework that capitalizes on their relative strengths. Shading data, however, is challenging to recover accurately from surfaces that contain texture. To address this issue, we propose an iterative technique that utilizes depth information to improve shading estimation, which in turn is used to elevate depth estimation in the presence of textures. With this approach, we demonstrate improvements over existing DFD techniques, as well as effective shape reconstruction of textureless surfaces.
Sparse Output Coding for Large-Scale Visual Recognition
Authors: Bin Zhao, Eric P. Xing
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Classification, General Classification, Multi-class Classification, Object Recognition, Scene Classification

Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.
Image Segmentation by Cascaded Region Agglomeration
Authors: Zhile Ren, Gregory Shakhnarovich
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We propose a hierarchical segmentation algorithm that starts with a very fine oversegmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense oversegmentation (superpixel) regime under boundary-based measures.
Spatial Inference Machines
Authors: Roman Shapovalov, Dmitry Vetrov, Pushmeet Kohli
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Segmentation, Semantic Segmentation

This paper addresses the problem of semantic segmentation of 3D point clouds. We extend the inference machines framework of Ross et al. by adding spatial factors that model mid-range and long-range dependencies inherent in the data. The new model is able to account for semantic spatial context. During training, our method automatically isolates and retains factors modelling spatial dependencies between variables that are relevant for achieving higher prediction accuracy. We evaluate the proposed method by using it to predict 17-category semantic segmentations on sets of stitched Kinect scans. Experimental results show that the spatial dependencies learned by our method significantly improve the accuracy of segmentation. They also show that our method outperforms the existing segmentation technique of Koppula et al.
Can a Fully Unconstrained Imaging Model Be Applied Effectively to Central Cameras?
Authors: Filippo Bergamasco, Andrea Albarelli, Emanuele Rodola, Andrea Torsello
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process. These considerations led to the definition of several ad hoc models that best adapt to different imaging devices, ranging from pinhole cameras with no radial distortion to the more complex catadioptric or polydioptric optics. In this paper we propose the use of an unconstrained model even in standard central camera settings dominated by the pinhole model, and introduce a novel calibration approach that can deal effectively with the huge number of free parameters associated with it, resulting in a higher precision calibration than what is possible with the standard pinhole model with correction for radial distortion. This effectively extends the use of general models to settings that traditionally have been ruled by parametric approaches out of practical considerations. The benefit of such an unconstrained model to quasipinhole central cameras is supported by an extensive experimental validation.
Learning Compact Binary Codes for Visual Tracking
Authors: Xi Li, Chunhua Shen, Anthony Dick, Anton Van Den Hengel
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Visual Tracking

A key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes. To attain this robustness, increasingly complex models are used to capture appearance variations. However, such models can be difficult to maintain accurately and efficiently. In this paper, we propose a visual tracker in which objects are represented by compact and discriminative binary codes. This representation can be processed very efficiently, and is capable of effectively fusing information from multiple cues. An incremental discriminative learner is then used to construct an appearance model that optimally separates the object from its surrounds. Furthermore, we design a hypergraph propagation method to capture the contextual information on samples, which further improves the tracking accuracy. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.
Efficient Maximum Appearance Search for Large-Scale Object Detection
Authors: Qiang Chen, Zheng Song, Rogerio Feris, Ankur Datta, Liangliang Cao, Zhongyang Huang, Shuicheng Yan
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: CPU, Object, object-detection, Object Detection

In recent years, efficiency of large-scale object detection has arisen as an important topic due to the exponential growth in the size of benchmark object detection datasets. Most current object detection methods focus on improving accuracy of large-scale object detection with efficiency being an afterthought. In this paper, we present the Efficient Maximum Appearance Search (EMAS) model which is an order of magnitude faster than the existing state-of-the-art large-scale object detection approaches, while maintaining comparable accuracy. Our EMAS model consists of representing an image as an ensemble of densely sampled feature points with the proposed Pointwise Fisher Vector encoding method, so that the learnt discriminative scoring function can be applied locally. Consequently, the object detection problem is transformed into searching an image sub-area for maximum local appearance probability, thereby making EMAS an order of magnitude faster than the traditional detection methods. In addition, the proposed model is also suitable for incorporating global context at a negligible extra computational cost. EMAS can also incorporate fusion of multiple features, which greatly improves its performance in detecting multiple object categories. Our experiments show that the proposed algorithm can perform detection of 1000 object classes in less than one minute per image on the Image Net ILSVRC2012 dataset and for 107 object classes in less than 5 seconds per image for the SUN09 dataset using a single CPU.