mtybilly/Instruct2DS huggingface dataset:
Efficient 3D Scene Labeling Using Fields of Trees
Authors: Olaf Kahler, Ian Reid
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We address the problem of 3D scene labeling in a structured learning framework. Unlike previous work which uses structured Support Vector Machines, we employ the recently described Decision Tree Field and Regression Tree Field frameworks, which learn the unary and binary terms of a Conditional Random Field from training data. We show this has significant advantages in terms of inference speed, while maintaining similar accuracy. We also demonstrate empirically the importance for overall labeling accuracy of features that make use of prior knowledge about the coarse scene layout such as the location of the ground plane. We show how this coarse layout can be estimated by our framework automatically, and that this information can be used to bootstrap improved accuracy in the detailed labeling.
Efficient Hand Pose Estimation from a Single Depth Image
Authors: Chi Xu, Li Cheng
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location; Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features; Verification step delivers the final 3D hand pose as the solution to an optimization problem. We analyze the depth noises, and suggest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinecttype noisy depth images, and reliably produces pose estimations of general motions efficiently (12 frames per second). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.
Curvature-Aware Regularization on Riemannian Submanifolds
Authors: Kwang In Kim, James Tompkin, Christian Theobalt
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: One fundamental assumption in object recognition as well as in other computer vision and pattern recognition problems is that the data generation process lies on a manifold and that it respects the intrinsic geometry of the manifold. This assumption is held in several successful algorithms for diffusion and regularization, in particular, in graph-Laplacian-based algorithms. We claim that the performance of existing algorithms can be improved if we additionally account for how the manifold is embedded within the ambient space, i.e., if we consider the extrinsic geometry of the manifold. We present a procedure for characterizing the extrinsic (as well as intrinsic) curvature of a manifold M which is described by a sampled point cloud in a high-dimensional Euclidean space. Once estimated, we use this characterization in general diffusion and regularization on M , and form a new regularizer on a point cloud. The resulting re-weighted graph Laplacian demonstrates superior performance over classical graph Laplacian in semisupervised learning and spectral clustering.
From Subcategories to Visual Composites: A Multi-level Framework for Object Detection
Authors: Tian Lan, Michalis Raptis, Leonid Sigal, Greg Mori
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The appearance of an object changes profoundly with pose, camera view and interactions of the object with other objects in the scene. This makes it challenging to learn detectors based on an object-level label (e.g., "car"). We postulate that having a richer set of labelings (at different levels of granularity) for an object, including finer-grained subcategories, consistent in appearance and view, and higherorder composites contextual groupings of objects consistent in their spatial layout and appearance, can significantly alleviate these problems. However, obtaining such a rich set of annotations, including annotation of an exponentially growing set of object groupings, is simply not feasible. We propose a weakly-supervised framework for object detection where we discover subcategories and the composites automatically with only traditional object-level category labels as input. To this end, we first propose an exemplar-SVM-based clustering approach, with latent SVM refinement, that discovers a variable length set of discriminative subcategories for each object class. We then develop a structured model for object detection that captures interactions among object subcategories and automatically discovers semantically meaningful and discriminatively relevant visual composites. We show that this model produces state-of-the-art performance on UIUC phrase object detection benchmark.
A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks
Authors: Yi-Lei Chen, Chiou-Ting Hsu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a novel low-rank appearance model for removing rain streaks. Different from previous work, our method needs neither rain pixel detection nor time-consuming dictionary learning stage. Instead, as rain streaks usually reveal similar and repeated patterns on imaging scene, we propose and generalize a low-rank model from matrix to tensor structure in order to capture the spatio-temporally correlated rain streaks. With the appearance model, we thus remove rain streaks from image/video (and also other high-order image structure) in a unified way. Our experimental results demonstrate competitive (or even better) visual quality and efficient run-time in comparison with state of the art.
Parallel Transport of Deformations in Shape Space of Elastic Surfaces
Authors: Qian Xie, Sebastian Kurtek, Huiling Le, Anuj Srivastava
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Statistical shape analysis develops methods for comparisons, deformations, summarizations, and modeling of shapes in given data sets. These tasks require a fundamental tool called parallel transport of tangent vectors along arbitrary paths. This tool is essential for: (1) computation of geodesic paths using either shooting or path-straightening method, (2) transferring deformations across objects, and (3) modeling of statistical variability in shapes. Using the square-root normal field (SRNF) representation of parameterized surfaces, we present a method for transporting deformations along paths in the shape space. This is difficult despite the underlying space being a vector space because the chosen (elastic) Riemannian metric is non-standard. Using a finite-basis for representing SRNFs of shapes, we derive expressions for Christoffel symbols that enable parallel transports. We demonstrate this framework using examples from shape analysis of parameterized spherical surfaces, in the three contexts mentioned above.
Human Attribute Recognition by Rich Appearance Dictionary
Authors: Jungseock Joo, Shuo Wang, Song-Chun Zhu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a part-based approach to the problem of human attribute recognition from a single image of a human body. To recognize the attributes of human from the body parts, it is important to reliably detect the parts. This is a challenging task due to the geometric variation such as articulation and view-point changes as well as the appearance variation of the parts arisen from versatile clothing types. The prior works have primarily focused on handling geometric variation by relying on pre-trained part detectors or pose estimators, which require manual part annotation, but the appearance variation has been relatively neglected in these works. This paper explores the importance of the appearance variation, which is directly related to the main task, attribute recognition. To this end, we propose to learn a rich appearance part dictionary of human with significantly less supervision by decomposing image lattice into overlapping windows at multiscale and iteratively refining local appearance templates. We also present quantitative results in which our proposed method outperforms the existing approaches.
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
Authors: Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We address the problem of localisation of objects as bounding boxes in images with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. We propose a novel framework based on Bayesian joint topic modelling. Our framework has three distinctive advantages over previous works: (1) All object classes and image backgrounds are modelled jointly together in a single generative model so that "explaining away" inference can resolve ambiguity and lead to better learning and localisation. (2) The Bayesian formulation of the model enables easy integration of prior knowledge about object appearance to compensate for limited supervision. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Extensive experiments on the challenging VOC dataset demonstrate that our approach outperforms the state-of-the-art competitors.
Frustratingly Easy NBNN Domain Adaptation
Authors: Tatiana Tommasi, Barbara Caputo
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Over the last years, several authors have signaled that state of the art categorization methods fail to perform well when trained and tested on data from different databases. The general consensus in the literature is that this issue, known as domain adaptation and/or dataset bias, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. The large majority of these works use BOW feature descriptors, and learning methods based on imageto-image distance functions. Following the seminal work of [6], in this paper we challenge these two assumptions. We experimentally show that using the NBNN classifier over existing domain adaptation databases achieves always very strong performances. We build on this result, and present an NBNN-based domain adaptation algorithm that learns iteratively a class metric while inducing, for each sample, a large margin separation among classes. To the best of our knowledge, this is the first work casting the domain adaptation problem within the NBNN framework. Experiments show that our method achieves the state of the art, both in the unsupervised and semi-supervised settings.
Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition
Authors: Joao F. Henriques, Joao Carreira, Rui Caseiro, Jorge Batista
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Competitive sliding window detectors require vast training sets. Since a pool of natural images provides a nearly endless supply of negative samples, in the form of patches at different scales and locations, training with all the available data is considered impractical. A staple of current approaches is hard negative mining, a method of selecting relevant samples, which is nevertheless expensive. Given that samples at slightly different locations have overlapping support, there seems to be an enormous amount of duplicated work. It is natural, then, to ask whether these redundancies can be eliminated. In this paper, we show that the Gram matrix describing such data is block-circulant. We derive a transformation based on the Fourier transform that block-diagonalizes the Gram matrix, at once eliminating redundancies and partitioning the learning problem. This decomposition is valid for any dense features and several learning algorithms, and takes full advantage of modern parallel architectures. Surprisingly, it allows training with all the potential samples in sets of thousands of images. By considering the full set, we generate in a single shot the optimal solution, which is usually obtained only after several rounds of hard negative mining. We report speed gains on Caltech Pedestrians and INRIA Pedestrians of over an order of magnitude, allowing training on a desktop computer in a couple of minutes.
Cross-Field Joint Image Restoration via Scale Map
Authors: Qiong Yan, Xiaoyong Shen, Li Xu, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Jiaya Jia
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Color, infrared, and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images in different fields, for example, one noisy color image and one dark-flashed nearinfrared image. The major issue in such a framework is to handle structure divergence and find commonly usable edges and smooth transition for visually compelling image reconstruction. We introduce a scale map as a competent representation to explicitly model derivative-level confidence and propose new functions and a numerical solver to effectively infer it following new structural observations. Our method is general and shows a principled way for cross-field restoration.
STAR3D: Simultaneous Tracking and Reconstruction of 3D Objects Using RGB-D Data
Authors: Carl Yuheng Ren, Victor Prisacariu, David Murray, Ian Reid
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We introduce a probabilistic framework for simultaneous tracking and reconstruction of 3D rigid objects using an RGB-D camera. The tracking problem is handled using a bag-of-pixels representation and a back-projection scheme. Surface and background appearance models are learned online, leading to robust tracking in the presence of heavy occlusion and outliers. In both our tracking and reconstruction modules, the 3D object is implicitly embedded using a 3D level-set function. The framework is initialized with a simple shape primitive model (e.g. a sphere or a cube), and the real 3D object shape is tracked and reconstructed online. Unlike existing depth-based 3D reconstruction works, which either rely on calibrated/fixed camera set up or use the observed world map to track the depth camera, our framework can simultaneously track and reconstruct small moving objects. We use both qualitative and quantitative results to demonstrate the superior performance of both tracking and reconstruction of our method.
Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery Using Multi-directional Oriented Flux
Authors: Engin Turetken, Carlos Becker, Przemyslaw Glowacki, Fethallah Benmansour, Pascal Fua
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose a new approach to detecting irregular curvilinear structures in noisy image stacks. In contrast to earlier approaches that rely on circular models of the crosssections, ours allows for the arbitrarily-shaped ones that are prevalent in biological imagery. This is achieved by maximizing the image gradient flux along multiple directions and radii, instead of only two with a unique radius as is usually done. This yields a more complex optimization problem for which we propose a computationally efficient solution. We demonstrate the effectiveness of our approach on a wide range of challenging gray scale and color datasets and show that it outperforms existing techniques, especially on very irregular structures.
Learning Slow Features for Behaviour Analysis
Authors: Lazaros Zafeiriou, Mihalis A. Nicolaou, Stefanos Zafeiriou, Symeon Nikitidis, Maja Pantic
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative approximation of the input signal finds uncorrelated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In particular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Maximization (EM) algorithm to perform inference in a probabilistic formulation of SFA and similarly extend it in order to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EMSFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping techniques for robust sequence timealignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.
Multi-view Object Segmentation in Space and Time
Authors: Abdelaziz Djelouah, Jean-Sebastien Franco, Edmond Boyer, Francois Le Clerc, Patrick Perez
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we address the problem of object segmentation in multiple views or videos when two or more viewpoints of the same scene are available. We propose a new approach that propagates segmentation coherence information in both space and time, hence allowing evidences in one image to be shared over the complete set. To this aim the segmentation is cast as a single efficient labeling problem over space and time with graph cuts. In contrast to most existing multi-view segmentation methods that rely on some form of dense reconstruction, ours only requires a sparse 3D sampling to propagate information between viewpoints. The approach is thoroughly evaluated on standard multiview datasets, as well as on videos. With static views, results compete with state of the art methods but they are achieved with significantly fewer viewpoints. With multiple videos, we report results that demonstrate the benefit of segmentation propagation through temporal cues.
Non-convex P-Norm Projection for Robust Sparsity
Authors: Mithun Das Gupta, Sanjeev Kumar
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we investigate the properties of L p norm (p Ittiswithin a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for L p norm projection on the non-negative simplex. We compare with L 1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification proposed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.
Robust Matrix Factorization with Unknown Noise
Authors: Deyu Meng, Fernando De La Torre
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Many problems in computer vision can be posed as recovering a low-dimensional subspace from highdimensional visual data. Factorization approaches to lowrank subspace estimation minimize a loss function between an observed measurement matrix and a bilinear factorization. Most popular loss functions include the L 2 and L 1 losses. L 2 is optimal for Gaussian noise, while L 1 is for Laplacian distributed noise. However, real data is often corrupted by an unknown noise distribution, which is unlikely to be purely Gaussian or Laplacian. To address this problem, this paper proposes a low-rank matrix factorization problem with a Mixture of Gaussians (MoG) noise model. The MoG model is a universal approximator for any continuous distribution, and hence is able to model a wider range of noise distributions. The parameters of the MoG model can be estimated with a maximum likelihood method, while the subspace is computed with standard approaches. We illustrate the benefits of our approach in extensive synthetic and real-world experiments including structure from motion, face modeling and background subtraction.
A General Two-Step Approach to Learning-Based Hashing
Authors: Guosheng Lin, Chunhua Shen, David Suter, Anton van den Hengel
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problemspecific hashing methods. Our framework decomposes the hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art.
Dynamic Scene Deblurring
Authors: Tae Hyun Kim, Byeongjoo Ahn, Kyoung Mu Lee
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Most conventional single image deblurring methods assume that the underlying scene is static and the blur is caused by only camera shake. In this paper, in contrast to this restrictive assumption, we address the deblurring problem of general dynamic scenes which contain multiple moving objects as well as camera shake. In case of dynamic scenes, moving objects and background have different blur motions, so the segmentation of the motion blur is required for deblurring each distinct blur motion accurately. Thus, we propose a novel energy model designed with the weighted sum of multiple blur data models, which estimates different motion blurs and their associated pixelwise weights, and resulting sharp image. In this framework, the local weights are determined adaptively and get high values when the corresponding data models have high data fidelity. And, the weight information is used for the segmentation of the motion blur. Non-local regularization of weights are also incorporated to produce more reliable segmentation results. A convex optimization-based method is used for the solution of the proposed energy model. Experimental results demonstrate that our method outperforms conventional approaches in deblurring both dynamic scenes and static scenes.
Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items
Authors: Kota Yamaguchi, M. Hadi Kiapour, Tamara L. Berg
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Clothing recognition is an extremely challenging problem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to parse the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse masks (paper doll item transfer) from retrieved examples. Experimental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.
Deep Learning Identity-Preserving Face Space
Authors: Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Face recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a new learningbased face representation: the face identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining discriminativeness between identities. Moreover, the FIP features extracted from an image under any pose and illumination can be used to reconstruct its face image in the canonical view. This property makes it possible to improve the performance of traditional descriptors, such as LBP [2] and Gabor [31], which can be extracted from our reconstructed images in the canonical view to eliminate variations. In order to learn the FIP features, we carefully design a deep network that combines the feature extraction layers and the reconstruction layer. The former encodes a face image into the FIP features, while the latter transforms them to an image in the canonical view. Extensive experiments on the large MultiPIE face database [7] demonstrate that it significantly outperforms the state-of-the-art face recognition methods.
Predicting Primary Gaze Behavior Using Social Saliency Fields
Authors: Hyun Soo Park, Eakta Jain, Yaser Sheikh
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a method to predict primary gaze behavior in a social scene. Inspired by the study of electric fields, we posit "social charges"--latent quantities that drive the primary gaze behavior of members of a social group. These charges induce a gradient field that defines the relationship between the social charges and the primary gaze direction of members in the scene. This field model is used to predict primary gaze behavior at any location or time in the scene. We present an algorithm to estimate the time-varying behavior of these charges from the primary gaze behavior of measured observers in the scene. We validate the model by evaluating its predictive precision via cross-validation in a variety of social scenes.
A Flexible Scene Representation for 3D Reconstruction Using an RGB-D Camera
Authors: Diego Thomas, Akihiro Sugimoto
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Updating a global 3D model with live RGB-D measurements has proven to be successful for 3D reconstruction of indoor scenes. Recently, a Truncated Signed Distance Function (TSDF) volumetric model and a fusion algorithm have been introduced (KinectFusion), showing significant advantages such as computational speed and accuracy of the reconstructed scene. This algorithm, however, is expensive in memory when constructing and updating the global model. As a consequence, the method is not well scalable to large scenes. We propose a new flexible 3D scene representation using a set of planes that is cheap in memory use and, nevertheless, achieves accurate reconstruction of indoor scenes from RGB-D image sequences. Projecting the scene onto different planes reduces significantly the size of the scene representation and thus it allows us to generate a global textured 3D model with lower memory requirement while keeping accuracy and easiness to update with live RGB-D measurements. Experimental results demonstrate that our proposed flexible 3D scene representation achieves accurate reconstruction, while keeping the scalability for large indoor scenes.
Multi-view Normal Field Integration for 3D Reconstruction of Mirroring Objects
Authors: Michael Weinmann, Aljosa Osep, Roland Ruiters, Reinhard Klein
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we present a novel, robust multi-view normal field integration technique for reconstructing the full 3D shape of mirroring objects. We employ a turntablebased setup with several cameras and displays. These are used to display illumination patterns which are reflected by the object surface. The pattern information observed in the cameras enables the calculation of individual volumetric normal fields for each combination of camera, display and turntable angle. As the pattern information might be blurred depending on the surface curvature or due to nonperfect mirroring surface characteristics, we locally adapt the decoding to the finest still resolvable pattern resolution. In complex real-world scenarios, the normal fields contain regions without observations due to occlusions and outliers due to interreflections and noise. Therefore, a robust reconstruction using only normal information is challenging. Via a non-parametric clustering of normal hypotheses derived for each point in the scene, we obtain both the most likely local surface normal and a local surface consistency estimate. This information is utilized in an iterative mincut based variational approach to reconstruct the surface geometry.
Exemplar-Based Graph Matching for Robust Facial Landmark Localization
Authors: Feng Zhou, Jonathan Brandt, Zhe Lin
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Localizing facial landmarks is a fundamental step in facial image analysis. However, the problem is still challenging due to the large variability in pose and appearance, and the existence of occlusions in real-world face images. In this paper, we present exemplar-based graph matching (EGM), a robust framework for facial landmark localization. Compared to conventional algorithms, EGM has three advantages: (1) an affine-invariant shape constraint is learned online from similar exemplars to better adapt to the test face; (2) the optimal landmark configuration can be directly obtained by solving a graph matching problem with the learned shape constraint; (3) the graph matching problem can be optimized efficiently by linear programming. To our best knowledge, this is the first attempt to apply a graph matching technique for facial landmark localization. Experiments on several challenging datasets demonstrate the advantages of EGM over state-of-the-art methods.
Space-Time Tradeoffs in Photo Sequencing
Authors: Tali Dekel (Basha), Yael Moses, Shai Avidan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Photo-sequencing is the problem of recovering the temporal order of a set of still images of a dynamic event, taken asynchronously by a set of uncalibrated cameras. Solving this problem is a first, crucial step for analyzing (or visualizing) the dynamic content of the scene captured by a large number of freely moving spectators. We propose a geometric based solution, followed by rank aggregation to the photo-sequencing problem. Our algorithm trades spatial certainty for temporal certainty. Whereas the previous solution proposed by [4] relies on two images taken from the same static camera to eliminate uncertainty in space, we drop the static-camera assumption and replace it with temporal information available from images taken from the same (moving) camera. Our method thus overcomes the limitation of the static-camera assumption, and scales much better with the duration of the event and the spread of cameras in space. We present successful results on challenging real data sets and large scale synthetic data (250 images).
Estimating Human Pose with Flowing Puppets
Authors: Silvia Zuffi, Javier Romero, Cordelia Schmid, Michael J. Black
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We address the problem of upper-body human pose estimation in uncontrolled monocular video sequences, without manual initialization. Most current methods focus on isolated video frames and often fail to correctly localize arms and hands. Inferring pose over a video sequence is advantageous because poses of people in adjacent frames exhibit properties of smooth variation due to the nature of human and camera motion. To exploit this, previous methods have used prior knowledge about distinctive actions or generic temporal priors combined with static image likelihoods to track people in motion. Here we take a different approach based on a simple observation: Information about how a person moves from frame to frame is present in the optical flow field. We develop an approach for tracking articulated motions that "links" articulated shape models of people in adjacent frames through the dense optical flow. Key to this approach is a 2D shape model of the body that we use to compute how the body moves over time. The resulting "flowing puppets" provide a way of integrating image evidence across frames to improve pose inference. We apply our method on a challenging dataset of TV video sequences and show state-of-the-art performance.
Action and Event Recognition with Fisher Vectors on a Compact Feature Set
Authors: Dan Oneata, Jakob Verbeek, Cordelia Schmid
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Action recognition in uncontrolled video is an important and challenging computer vision problem. Recent progress in this area is due to new local features and models that capture spatio-temporal structure between local features, or human-object interactions. Instead of working towards more complex models, we focus on the low-level features and their encoding. We evaluate the use of Fisher vectors as an alternative to bag-of-word histograms to aggregate a small set of state-of-the-art low-level descriptors, in combination with linear classifiers. We present a large and varied set of evaluations, considering (i) classification of short actions in five datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that for basic action recognition and localization MBH features alone are enough for stateof-the-art performance. For complex events we find that SIFT and MFCC features provide complementary cues. On all three problems we obtain state-of-the-art results, while using fewer features and less complex models.
Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition
Authors: Hans Lobel, Rene Vidal, Alvaro Soto
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Currently, Bag-of-Visual-Words (BoVW) and part-based methods are the most popular approaches for visual recognition. In both cases, a mid-level representation is built on top of low-level image descriptors and top-level classifiers use this mid-level representation to achieve visual recognition. While in current part-based approaches, midand top-level representations are usually jointly trained, this is not the usual case for BoVW schemes. A main reason for this is the complex data association problem related to the usual large dictionary size needed by BoVW approaches. As a further observation, typical solutions based on BoVW and part-based representations are usually limited to extensions of binary classification schemes, a strategy that ignores relevant correlations among classes. In this work we propose a novel hierarchical approach to visual recognition based on a BoVW scheme that jointly learns suitable midand top-level representations. Furthermore, using a maxmargin learning framework, the proposed approach directly handles the multiclass case at both levels of abstraction. We test our proposed method using several popular benchmark datasets. As our main result, we demonstrate that, by coupling learning of midand top-level representations, the proposed approach fosters sharing of discriminative visual words among target classes, being able to achieve state-ofthe-art recognition performance using far less visual words than previous approaches.
Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics
Authors: Nicolas Riche, Matthieu Duvinage, Matei Mancas, Bernard Gosselin, Thierry Dutoit
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Visual saliency has been an increasingly active research area in the last ten years with dozens of saliency models recently published. Nowadays, one of the big challenges in the field is to find a way to fairly evaluate all of these models. In this paper, on human eye fixations ,we compare the ranking of 12 state-of-the art saliency models using 12 similarity metrics. The comparison is done on Jian Li's database containing several hundreds of natural images. Based on Kendall concordance coefficient, it is shown that some of the metrics are strongly correlated leading to a redundancy in the performance metrics reported in the available benchmarks. On the other hand, other metrics provide a more diverse picture of models' overall performance. As a recommendation, three similarity metrics should be used to obtain a complete point of view of saliency model performance.
Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
Authors: Bo Wang, Zhuowen Tu, John K. Tsotsos
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label classification scheme, dynamic label propagation (DLP), which performs transductive learning through propagation in a dynamic process. Existing semi-supervised classification methods often have difficulty in dealing with multi-class/multi-label problems due to the lack in consideration of label correlation; our algorithm instead emphasizes dynamic metric fusion with label information. Significant improvement over the state-of-the-art methods is observed on benchmark datasets for both multiclass and multi-label tasks.
Learning Discriminative Part Detectors for Image Classification and Cosegmentation
Authors: Jian Sun, Jean Ponce
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we address the problem of learning discriminative part detectors from image sets with category labels. We propose a novel latent SVM model regularized by group sparsity to learn these part detectors. Starting from a large set of initial parts, the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max-margin framework. We propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem. We apply the proposed method to image classification and cosegmentation, and quantitative experiments with standard benchmarks show that it matches or improves upon the state of the art.
Co-segmentation by Composition
Authors: Alon Faktor, Michal Irani
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Given a set of images which share an object from the same semantic category, we would like to co-segment the shared object. We define 'good' co-segments to be ones which can be easily composed (like a puzzle) from large pieces of other co-segments, yet are difficult to compose from remaining image parts. These pieces must not only match well but also be statistically significant (hard to compose at random). This gives rise to co-segmentation of objects in very challenging scenarios with large variations in appearance, shape and large amounts of clutter. We further show how multiple images can collaborate and "score" each others' co-segments to improve the overall fidelity and accuracy of the co-segmentation. Our co-segmentation can be applied both to large image collections, as well as to very few images (where there is too little data for unsupervised learning). At the extreme, it can be applied even to a single image, to extract its co-occurring objects. Our approach obtains state-of-the-art results on benchmark datasets. We further show very encouraging co-segmentation results on the challenging PASCAL-VOC dataset.
A New Image Quality Metric for Image Auto-denoising
Authors: Xiangfei Kong, Kuan Li, Qingxiong Yang, Liu Wenyin, Ming-Hsuan Yang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper proposes a new non-reference image quality metric that can be adopted by the state-of-the-art image/video denoising algorithms for auto-denoising. The proposed metric is extremely simple and can be implemented in four lines of Matlab code 1 . The basic assumption employed by the proposed metric is that the noise should be independent of the original image. A direct measurement of this dependence is, however, impractical due to the relatively low accuracy of existing denoising method. The proposed metric thus aims at maximizing the structure similarity between the input noisy image and the estimated image noise around homogeneous regions and the structure similarity between the input noisy image and the denoised image around highly-structured regions, and is computed as the linear correlation coefficient of the two corresponding structure similarity maps. Numerous experimental results demonstrate that the proposed metric not only outperforms the current state-of-the-art non-reference quality metric quantitatively and qualitatively, but also better maintains temporal coherence when used for video denoising.
Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition
Authors: Yizhe Zhang, Ming Shao, Edward K. Wong, Yun Fu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the registered faces. In this paper, we propose a high-level feature learning scheme to extract pose-invariant identity feature for face recognition. First, we build a single-hiddenlayer neural network with sparse constraint, to extract poseinvariant feature in a supervised fashion. Second, we further enhance the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders. By enforcing the target values to be unique for input faces over different poses, the learned highlevel feature that is represented by the neurons in the hidden layer is pose free and only relevant to the identity information. Finally, we conduct face identification on CMU MultiPIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve are reported. These experiments demonstrate that our model is superior to other state-of-the-art approaches on handling pose variations.
Text Localization in Natural Images Using Stroke Feature Transform and Text Covariance Descriptors
Authors: Weilin Huang, Zhe Lin, Jianchao Yang, Jue Wang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we present a new approach for text localization in natural images, by discriminating text and non-text regions at three levels: pixel, component and textline levels. Firstly, a powerful low-level filter called the Stroke Feature Transform (SFT) is proposed, which extends the widely-used Stroke Width Transform (SWT) by incorporating color cues of text pixels, leading to significantly enhanced performance on inter-component separation and intra-component connection. Secondly, based on the output of SFT, we apply two classifiers, a text component classifier and a text-line classifier, sequentially to extract text regions, eliminating the heuristic procedures that are commonly used in previous approaches. The two classifiers are built upon two novel Text Covariance Descriptors (TCDs) that encode both the heuristic properties and the statistical characteristics of text stokes. Finally, text regions are located by simply thresholding the text-line confident map. Our method was evaluated on two benchmark datasets: ICDAR 2005 and ICDAR 2011, and the corresponding Fmeasure values are 0.72 and 0.73, respectively, surpassing previous methods in accuracy by a large margin.
Modeling the Calibration Pipeline of the Lytro Camera for High Quality Light-Field Image Reconstruction
Authors: Donghyeon Cho, Minhaeng Lee, Sunyeong Kim, Yu-Wing Tai
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Light-field imaging systems have got much attention recently as the next generation camera model. A light-field imaging system consists of three parts: data acquisition, manipulation, and application. Given an acquisition system, it is important to understand how a light-field camera converts from its raw image to its resulting refocused image. In this paper, using the Lytro camera as an example, we describe step-by-step procedures to calibrate a raw light-field image. In particular, we are interested in knowing the spatial and angular coordinates of the micro lens array and the resampling process for image reconstruction. Since Lytro uses a hexagonal arrangement of a micro lens image, additional treatments in calibration are required. After calibration, we analyze and compare the performances of several resampling methods for image reconstruction with and without calibration. Finally, a learning based interpolation method is proposed which demonstrates a higher quality image reconstruction than previous interpolation methods including a method used in Lytro software.
Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes
Authors: Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Although graph matching is a fundamental problem in pattern recognition, and has drawn broad interest from many fields, the problem of learning graph matching has not received much attention. In this paper, we redefine the learning of graph matching as a model learning problem. In addition to conventional training of matching parameters, our approach modifies the graph structure and attributes to generate a graphical model. In this way, the model learning is oriented toward both matching and recognition performance, and can proceed in an unsupervised gnfashion. Experiments demonstrate that our approach outperforms conventional methods for learning graph matching.