mtybilly/Instruct2DS huggingface dataset:
Joint Subspace Stabilization for Stereoscopic Video
Authors: Feng Liu, Yuzhen Niu, Hailin Jin
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Shaky stereoscopic video is not only unpleasant to watch but may also cause 3D fatigue. Stabilizing the left and right view of a stereoscopic video separately using a monocular stabilization method tends to both introduce undesirable vertical disparities and damage horizontal disparities, which may destroy the stereoscopic viewing experience. In this paper, we present a joint subspace stabilization method for stereoscopic video. We prove that the low-rank subspace constraint for monocular video [10] also holds for stereoscopic video. Particularly, the feature trajectories from the left and right video share the same subspace. Based on this proof, we develop a stereo subspace stabilization method that jointly computes a common subspace from the left and right video and uses it to stabilize the two videos simultaneously. Our method meets the stereoscopic constraints without 3D reconstruction or explicit left-right correspondence. We test our method on a variety of stereoscopic videos with different scene content and camera motion. The experiments show that our method achieves high-quality stabilization for stereoscopic video in a robust and efficient way.
Learning CRFs for Image Parsing with Adaptive Subgradient Descent
Authors: Honghui Zhang, Jingdong Wang, Ping Tan, Jinglu Wang, Long Quan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose an adaptive subgradient descent method to efficiently learn the parameters of CRF models for image parsing. To balance the learning efficiency and performance of the learned CRF models, the parameter learning is iteratively carried out by solving a convex optimization problem in each iteration, which integrates a proximal term to preserve the previously learned information and the large margin preference to distinguish bad labeling and the ground truth labeling. A solution of subgradient descent updating form is derived for the convex optimization problem, with an adaptively determined updating step-size. Besides, to deal with partially labeled training data, we propose a new objective constraint modeling both the labeled and unlabeled parts in the partially labeled training data for the parameter learning of CRF models. The superior learning efficiency of the proposed method is verified by the experiment results on two public datasets. We also demonstrate the powerfulness of our method for handling partially labeled training data.
Box in the Box: Joint 3D Layout and Object Reasoning from Single Images
Authors: Alexander G. Schwing, Sanja Fidler, Marc Pollefeys, Raquel Urtasun
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper we propose an approach to jointly infer the room layout as well as the objects present in the scene. Towards this goal, we propose a branch and bound algorithm which is guaranteed to retrieve the global optimum of the joint problem. The main difficulty resides in taking into account occlusion in order to not over-count the evidence. We introduce a new decomposition method, which generalizes integral geometry to triangular shapes, and allows us to bound the different terms in constant time. We exploit both geometric cues and object detectors as image features and show large improvements in 2D and 3D object detection over state-of-the-art deformable part-based models.
A Global Linear Method for Camera Pose Registration
Authors: Nianjuan Jiang, Zhaopeng Cui, Ping Tan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a linear method for global camera pose registration from pairwise relative poses encoded in essential matrices. Our method minimizes an approximate geometric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typical 'unbalanced scale' problem in linear methods relying on pairwise translation direction constraints, i.e. an algebraic error; nor the system degeneracy from collinear motion. In the case of three cameras, our method provides a good linear approximation of the trifocal tensor. It can be directly scaled up to register multiple cameras. The results obtained are accurate for point triangulation and can serve as a good initialization for final bundle adjustment. We evaluate the algorithm performance with different types of data and demonstrate its effectiveness. Our system produces good accuracy, robustness, and outperforms some well-known systems on efficiency.
Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model
Authors: Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Automatic image categorization has become increasingly important with the development of Internet and the growth in the size of image databases. Although the image categorization can be formulated as a typical multiclass classification problem, two major challenges have been raised by the real-world images. On one hand, though using more labeled training data may improve the prediction performance, obtaining the image labels is a time consuming as well as biased process. On the other hand, more and more visual descriptors have been proposed to describe objects and scenes appearing in images and different features describe different aspects of the visual characteristics. Therefore, how to integrate heterogeneous visual features to do the semi-supervised learning is crucial for categorizing large-scale image data. In this paper, we propose a novel approach to integrate heterogeneous features by performing multi-modal semi-supervised classification on unlabeled as well as unsegmented images. Considering each type of feature as one modality, taking advantage of the large amount of unlabeled data information, our new adaptive multimodal semi-supervised classification (AMMSS) algorithm learns a commonly shared class indicator matrix and the weights for different modalities (image features) simultaneously.
3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding
Authors: Scott Satkin, Martial Hebert
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a new algorithm 3DNN (3D NearestNeighbor), which is capable of matching an image with 3D data, independently of the viewpoint from which the image was captured. By leveraging rich annotations associated with each image, our algorithm can automatically produce precise and detailed 3D models of a scene from a single image. Moreover, we can transfer information across images to accurately label and segment objects in a scene. The true benefit of 3DNN compared to a traditional 2D nearest-neighbor approach is that by generalizing across viewpoints, we free ourselves from the need to have training examples captured from all possible viewpoints. Thus, we are able to achieve comparable results using orders of magnitude less data, and recognize objects from never-beforeseen viewpoints. In this work, we describe the 3DNN algorithm and rigorously evaluate its performance for the tasks of geometry estimation and object detection/segmentation. By decoupling the viewpoint and the geometry of an image, we develop a scene matching approach which is truly 100% viewpoint invariant, yielding state-of-the-art performance on challenging data.
Correntropy Induced L2 Graph for Robust Subspace Clustering
Authors: Canyi Lu, Jinhui Tang, Min Lin, Liang Lin, Shuicheng Yan, Zhouchen Lin
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different regularization of the representation coefficient. We instead focus on the robustness of the model to non-Gaussian noises. We propose a new robust clustering method by using the correntropy induced metric, which is robust for handling the non-Gaussian and impulsive noises. Also we further extend the method for handling the data with outlier rows/features. The multiplicative form of half-quadratic optimization is used to optimize the nonconvex correntropy objective function of the proposed models. Extensive experiments on face datasets well demonstrate that the proposed methods are more robust to corruptions and occlusions.
Unsupervised Domain Adaptation by Domain Invariant Projection
Authors: Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and target domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a standard domain adaptation benchmark dataset.
Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences
Authors: Frank Steinbrucker, Christian Kerl, Daniel Cremers
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose a method to generate highly detailed, textured 3D models of large environments from RGB-D sequences. Our system runs in real-time on a standard desktop PC with a state-of-the-art graphics card. To reduce the memory consumption, we fuse the acquired depth maps and colors in a multi-scale octree representation of a signed distance function. To estimate the camera poses, we construct a pose graph and use dense image alignment to determine the relative pose between pairs of frames. We add edges between nodes when we detect loop-closures and optimize the pose graph to correct for long-term drift. Our implementation is highly parallelized on graphics hardware to achieve real-time performance. More specifically, we can reconstruct, store, and continuously update a colored 3D model of an entire corridor of nine rooms at high levels of detail in real-time on a single GPU with 2.5GB.
Detecting Curved Symmetric Parts Using a Deformable Disc Model
Authors: Tom Sie Ho Lee, Sanja Fidler, Sven Dickinson
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Symmetry is a powerful shape regularity that's been exploited by perceptual grouping researchers in both human and computer vision to recover part structure from an image without a priori knowledge of scene content. Drawing on the concept of a medial axis, defined as the locus of centers of maximal inscribed discs that sweep out a symmetric part, we model part recovery as the search for a sequence of deformable maximal inscribed disc hypotheses generated from a multiscale superpixel segmentation, a framework proposed by [13]. However, we learn affinities between adjacent superpixels in a space that's invariant to bending and tapering along the symmetry axis, enabling us to capture a wider class of symmetric parts. Moreover, we introduce a global cost that perceptually integrates the hypothesis space by combining a pairwise and a higher-level smoothing term, which we minimize globally using dynamic programming. The new framework is demonstrated on two datasets, and is shown to significantly outperform the baseline [13].
Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation
Authors: Yuandong Tian, Srinivasa G. Narasimhan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Real-world surfaces such as clothing, water and human body deform in complex ways. The image distortions observed are high-dimensional and non-linear, making it hard to estimate these deformations accurately. The recent datadriven descent approach [17] applies Nearest Neighbor estimators iteratively on a particular distribution of training samples to obtain a globally optimal and dense deformation field between a template and a distorted image. In this work, we develop a hierarchical structure for the Nearest Neighbor estimators, each of which can have only a local image support. We demonstrate in both theory and practice that this algorithm has several advantages over the nonhierarchical version: it guarantees global optimality with significantly fewer training samples, is several orders faster, provides a metric to decide whether a given image is "hard" (or "easy") requiring more (or less) samples, and can handle more complex scenes that include both global motion and local deformation. The proposed algorithm successfully tracks a broad range of non-rigid scenes including water, clothing, and medical images, and compares favorably against several other deformation estimation and tracking approaches that do not provide optimality guarantees.
Recognising Human-Object Interaction via Exemplar Based Modelling
Authors: Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, Shaogang Gong, Tao Xiang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Human action can be recognised from a single still image by modelling Human-object interaction (HOI), which infers the mutual spatial structure information between human and object as well as their appearance. Existing approaches rely heavily on accurate detection of human and object, and estimation of human pose. They are thus sensitive to large variations of human poses, occlusion and unsatisfactory detection of small size objects. To overcome this limitation, a novel exemplar based approach is proposed in this work. Our approach learns a set of spatial pose-object interaction exemplars, which are density functions describing how a person is interacting with a manipulated object for different activities spatially in a probabilistic way. A representation based on our HOI exemplar thus has great potential for being robust to the errors in human/object detection and pose estimation. A new framework consists of a proposed exemplar based HOI descriptor and an activity specific matching model that learns the parameters is formulated for robust human activity recognition. Experiments on two benchmark activity datasets demonstrate that the proposed approach obtains state-ofthe-art performance.
How Do You Tell a Blackbird from a Crow?
Authors: Thomas Berg, Peter N. Belhumeur
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: How do you tell a blackbird from a crow? There has been great progress toward automatic methods for visual recognition, including fine-grained visual categorization in which the classes to be distinguished are very similar. In a task such as bird species recognition, automatic recognition systems can now exceed the performance of non-experts most people are challenged to name a couple dozen bird species, let alone identify them. This leads us to the question, "Can a recognition system show humans what to look for when identifying classes (in this case birds)?" In the context of fine-grained visual categorization, we show that we can automatically determine which classes are most visually similar, discover what visual features distinguish very similar classes, and illustrate the key features in a way meaningful to humans. Running these methods on a dataset of bird images, we can generate a visual field guide to birds which includes a tree of similarity that displays the similarity relations between all species, pages for each species showing the most similar other species, and pages for each pair of similar species illustrating their differences.
Video Synopsis by Heterogeneous Multi-source Correlation
Authors: Xiatian Zhu, Chen Change Loy, Shaogang Gong
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Generating coherent synopsis for surveillance video stream remains a formidable challenge due to the ambiguity and uncertainty inherent to visual observations. In contrast to existing video synopsis approaches that rely on visual cues alone, we propose a novel multi-source synopsis framework capable of correlating visual data and independent non-visual auxiliary information to better describe and summarise subtle physical events in complex scenes. Specifically, our unsupervised framework is capable of seamlessly uncovering latent correlations among heterogeneous types of data sources, despite the non-trivial heteroscedasticity and dimensionality discrepancy problems. Additionally, the proposed model is robust to partial or missing non-visual information. We demonstrate the effectiveness of our framework on two crowded public surveillance datasets.
Semantic Segmentation without Annotating Segments
Authors: Wei Xia, Csaba Domokos, Jian Dong, Loong-Fah Cheong, Shuicheng Yan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated segments are available. Based on a set of segment hypotheses, we introduce a simple voting scheme to estimate shape guidance for each bounding box. The derived shape guidance is used in the subsequent graph-cut-based figure-ground segmentation. The final segmentation result is obtained by merging the segmentation results in the bounding boxes. We conduct an extensive analysis of the effect of object bounding box accuracy. Comprehensive experiments on both the challenging PASCAL VOC object segmentation dataset and GrabCut50 image segmentation dataset show that the proposed approach achieves competitive results compared to previous detection or bounding box prior based methods, as well as other state-of-the-art semantic segmentation methods.
Action Recognition with Actons
Authors: Jun Zhu, Baoyuan Wang, Xiaokang Yang, Wenjun Zhang, Zhuowen Tu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: With the improved accessibility to an exploding amount of video data and growing demands in a wide range of video analysis applications, video-based action recognition/classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to automatically exploit a mid-level "acton" representation. The weakly-supervised actons are learned via a new max-margin multi-channel multiple instance learning framework, which can capture multiple mid-level action concepts simultaneously. The learned actons (with no requirement for detailed manual annotations) observe the properties of being compact, informative, discriminative, and easy to scale. The experimental results demonstrate the effectiveness of applying the learned actons in our two-layer structure, and show the state-ofthe-art recognition performance on two challenging action datasets, i.e., Youtube and HMDB51.
Exemplar Cut
Authors: Jimei Yang, Yi-Hsuan Tsai, Ming-Hsuan Yang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a hybrid parametric and nonparametric algorithm, exemplar cut, for generating class-specific object segmentation hypotheses. For the parametric part, we train a pylon model on a hierarchical region tree as the energy function for segmentation. For the nonparametric part, we match the input image with each exemplar by using regions to obtain a score which augments the energy function from the pylon model. Our method thus generates a set of highly plausible segmentation hypotheses by solving a series of exemplar augmented graph cuts. Experimental results on the Graz and PASCAL datasets show that the proposed algorithm achieves favorable segmentation performance against the state-of-the-art methods in terms of visual quality and accuracy.
Discovering Object Functionality
Authors: Bangpeng Yao, Jiayuan Ma, Li Fei-Fei
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Object functionality refers to the quality of an object that allows humans to perform some specific actions. It has been shown in psychology that functionality (affordance) is at least as essential as appearance in object recognition by humans. In computer vision, most previous work on functionality either assumes exactly one functionality for each object, or requires detailed annotation of human poses and objects. In this paper, we propose a weakly supervised approach to discover all possible object functionalities. Each object functionality is represented by a specific type of human-object interaction. Our method takes any possible human-object interaction into consideration, and evaluates image similarity in 3D rather than 2D in order to cluster human-object interactions more coherently. Experimental results on a dataset of people interacting with musical instruments show the effectiveness of our approach.
Saliency Detection: A Boolean Map Approach
Authors: Jianming Zhang, Stan Sclaroff
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized by a set of binary images, which are generated by randomly thresholding the image's color channels. Based on a Gestalt principle of figure-ground segregation, BMS computes saliency maps by analyzing the topological structure of Boolean maps. BMS is simple to implement and efficient to run. Despite its simplicity, BMS consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets. Furthermore, BMS is also shown to be advantageous in salient object detection.
Active MAP Inference in CRFs for Efficient Semantic Segmentation
Authors: Gemma Roig, Xavier Boix, Roderick De Nijs, Sebastian Ramos, Koljia Kuhnlenz, Luc Van Gool
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects
Authors: Stefan Duffner, Christophe Garcia
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we present a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a probabilistic segmentation method based on global models for foreground and background. These components are used for tracking in a combined way, and they adapt each other in a co-training manner. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging standard videos, and outperforms state-of-theart tracking methods designed for the same task. Finally, the proposed models allow for an extremely efficient implementation, and thus tracking is very fast.
Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification
Authors: Mandar Dixit, Nikhil Rasiwasia, Nuno Vasconcelos
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: An extension of the latent Dirichlet allocation (LDA), denoted class-specific-simplex LDA (css-LDA), is proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification. To address this, we introduce a model that induces supervision in topic discovery, while retaining the original flexibility of LDA to account for unanticipated structures of interest. The proposed css-LDA is an LDA model with class supervision at the level of image features. In css-LDA topics are discovered per class, i.e. a single set of topics shared across classes is replaced by multiple class-specific topic sets. This model can be used for generative classification using the Bayes decision rule or even extended to discriminative classification with support vector machines (SVMs). A css-LDA model can endow an image with a vector of class and topic specific count statistics that are similar to the Bag-of-words (BoW) histogram. SVM-based discriminants can be learned for classes in the space of these histograms. The effectiveness of css-LDA model in both generative and discriminative classification frameworks is demonstrated through an extensive experimental evaluation, involving multiple benchmark datasets, where it is shown to outperform all existing LDA based image classification approaches.
BOLD Features to Detect Texture-less Objects
Authors: Federico Tombari, Alessandro Franchi, Luigi Di Stefano
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Object detection in images withstanding significant clutter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and distinctive representation of groups of neighboring line segments aggregated over limited spatial supports and invariant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlusion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects.
Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency
Authors: Jiongxin Liu, Peter N. Belhumeur
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a novel approach for bird part localization, targeting fine-grained categories with wide variations in appearance due to different poses (including aspect and orientation) and subcategories. As it is challenging to represent such variations across a large set of diverse samples with tractable parametric models, we turn to individual exemplars. Specifically, we extend the exemplarbased models in [4] by enforcing pose and subcategory consistency at the parts. During training, we build posespecific detectors scoring part poses across subcategories, and subcategory-specific detectors scoring part appearance across poses. At the testing stage, likely exemplars are matched to the image, suggesting part locations whose pose and subcategory consistency are well-supported by the image cues. From these hypotheses, part configuration can be predicted with very high accuracy. Experimental results demonstrate significant performance gains from our method on an extensive dataset: CUB-200-2011 [30], for both localization and classification tasks.
Multiple Non-rigid Surface Detection and Registration
Authors: Yi Wu, Yoshihisa Ijiri, Ming-Hsuan Yang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Detecting and registering nonrigid surfaces are two important research problems for computer vision. Much work has been done with the assumption that there exists only one instance in the image. In this work, we propose an algorithm that detects and registers multiple nonrigid instances of given objects in a cluttered image. Specifically, after we use low level feature points to obtain the initial matches between templates and the input image, a novel high-order affinity graph is constructed to model the consistency of local topology. A hierarchical clustering approach is then used to locate the nonrigid surfaces. To remove the outliers in the cluster, we propose a deterministic annealing approach based on the Thin Plate Spline (TPS) model. The proposed method achieves high accuracy even when the number of outliers is nineteen times larger than the inliers. As the matches may appear sparsely in each instance, we propose a TPS based match growing approach to propagate the matches. Finally, an approach that fuses feature and appearance information is proposed to register each nonrigid surface. Extensive experiments and evaluations demonstrate that the proposed algorithm achieves promising results in detecting and registering multiple non-rigid surfaces in a cluttered scene.
Drosophila Embryo Stage Annotation Using Label Propagation
Authors: Tomas Kazmar, Evgeny Z. Kvon, Alexander Stark, Christoph H. Lampert
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this work we propose a system for automatic classification of Drosophila embryos into developmental stages. While the system is designed to solve an actual problem in biological research, we believe that the principle underlying it is interesting not only for biologists, but also for researchers in computer vision. The main idea is to combine two orthogonal sources of information: one is a classifier trained on strongly invariant features, which makes it applicable to images of very different conditions, but also leads to rather noisy predictions. The other is a label propagation step based on a more powerful similarity measure that however is only consistent within specific subsets of the data at a time. In our biological setup, the information sources are the shape and the staining patterns of embryo images. We show experimentally that while neither of the methods can be used by itself to achieve satisfactory results, their combination achieves prediction quality comparable to human performance.
Parsing IKEA Objects: Fine Pose Estimation
Authors: Joseph J. Lim, Hamed Pirsiavash, Antonio Torralba
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We address the problem of localizing and estimating the fine-pose of objects in the image with exact 3D models. Our main focus is to unify contributions from the 1970s with recent advances in object detection: use local keypoint detectors to find candidate poses and score global alignment of each candidate pose to the image. Moreover, we also provide a new dataset containing fine-aligned objects with their exactly matched 3D models, and a set of models for widely used objects. We also evaluate our algorithm both on object detection and fine pose estimation, and show that our method outperforms state-of-the art algorithms.
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps
Authors: Jiajia Luo, Wei Wang, Hairong Qi
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free styles in performing actions, may cause large intra-class variations. In this paper, a new framework based on sparse coding and temporal pyramid matching (TPM) is proposed for depthbased human action recognition. Especially, a discriminative class-specific dictionary learning algorithm is proposed for sparse coding. By adding the group sparsity and geometry constraints, features can be well reconstructed by the sub-dictionary belonging to the same class, and the geometry relationships among features are also kept in the calculated coefficients. The proposed approach is evaluated on two benchmark datasets captured by depth cameras. Experimental results show that the proposed algorithm repeatedly achieves superior performance to the state of the art algorithms. Moreover, the proposed dictionary learning method also outperforms classic dictionary learning approaches.
Online Video SEEDS for Temporal Window Objectness
Authors: Michael Van Den Bergh, Gemma Roig, Xavier Boix, Santiago Manen, Luc Van Gool
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Superpixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algorithms have been extended to video in order to exploit the temporal consistency between frames. However, most methods are computationally too expensive for real-time applications. We introduce an online, real-time video superpixel algorithm based on the recently proposed SEEDS superpixels. A new capability is incorporated which delivers multiple diverse samples (hypotheses) of superpixels in the same image or video sequence. The multiple samples are shown to provide a strong cue to efficiently measure the objectness of image windows, and we introduce the novel concept of objectness in temporal windows. Experiments show that the video superpixels achieve comparable performance to state-of-the-art offline methods while running at 30 fps on a single 2.8 GHz i7 CPU. State-of-the-art performance on objectness is also demonstrated, yet orders of magnitude faster and extended to temporal windows in video.
Fast Subspace Search via Grassmannian Based Hashing
Authors: Xu Wang, Stefan Atev, John Wright, Gilad Lerman
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The problem of efficiently deciding which of a database of models is most similar to a given input query arises throughout modern computer vision. Motivated by applications in recognition, image retrieval and optimization, there has been significant recent interest in the variant of this problem in which the database models are linear subspaces and the input is either a point or a subspace. Current approaches to this problem have poor scaling in high dimensions, and may not guarantee sublinear query complexity. We present a new approach to approximate nearest subspace search, based on a simple, new locality sensitive hash for subspaces. Our approach allows point-tosubspace query for a database of subspaces of arbitrary dimension d, in a time that depends sublinearly on the number of subspaces in the database. The query complexity of our algorithm is linear in the ambient dimension D, allowing it to be directly applied to high-dimensional imagery data. Numerical experiments on model problems in image repatching and automatic face recognition confirm the advantages of our algorithm in terms of both speed and accuracy.
Data-Driven 3D Primitives for Single Image Understanding
Authors: David F. Fouhey, Abhinav Gupta, Martial Hebert
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: What primitives should we use to infer the rich 3D world behind an image? We argue that these primitives should be both visually discriminative and geometrically informative and we present a technique for discovering such primitives. We demonstrate the utility of our primitives by using them to infer 3D surface normals given a single image. Our technique substantially outperforms the state-of-the-art and shows improved cross-dataset performance.
Partial Enumeration and Curvature Regularization
Authors: Carl Olsson, Johannes Ulen, Yuri Boykov, Vladimir Kolmogorov
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Energies with high-order non-submodular interactions have been shown to be very useful in vision due to their high modeling power. Optimization of such energies, however, is generally NP-hard. A naive approach that works for small problem instances is exhaustive search, that is, enumeration of all possible labelings of the underlying graph. We propose a general minimization approach for large graphs based on enumeration of labelings of certain small patches. This partial enumeration technique reduces complex highorder energy formulations to pairwise Constraint Satisfaction Problems with unary costs (uCSP), which can be efficiently solved using standard methods like TRW-S. Our approach outperforms a number of existing state-of-the-art algorithms on well known difficult problems (e.g. curvature regularization, stereo, deconvolution); it gives near global minimum and better speed. Our main application of interest is curvature regularization. In the context of segmentation, our partial enumeration technique allows to evaluate curvature directly on small patches using a novel integral geometry approach. 1
Fast Face Detector Training Using Tailored Views
Authors: Kristina Scherbaum, James Petterson, Rogerio S. Feris, Volker Blanz, Hans-Peter Seidel
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does not guarantee that the collected training data is representative in terms of data variability. Moreover, available data sets are often acquired under controlled settings, restricting, for example, scene illumination or 3D head pose to a narrow range. This paper takes a look into the automated generation of adaptive training samples from a 3D morphable face model. Using statistical insights, the tailored training data guarantees full data variability and is enriched by arbitrary facial attributes such as age or body weight. Moreover, it can automatically adapt to environmental constraints, such as illumination or viewing angle of recorded video footage from surveillance cameras. We use the tailored imagery to train a new many-core implementation of Viola Jones' AdaBoost object detection framework. The new implementation is not only faster but also enables the use of multiple feature channels such as color features at training time. In our experiments we trained seven view-dependent face detectors and evaluate these on the Face Detection Data Set and Benchmark (FDDB). Our experiments show that the use of tailored training imagery outperforms state-of-the-art approaches on this challenging dataset.
Image Retrieval Using Textual Cues
Authors: Anand Mishra, Karteek Alahari, C.V. Jawahar
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present an approach for the text-to-image retrieval problem based on textual content present in images. Given the recent developments in understanding text in images, an appealing approach to address this problem is to localize and recognize the text, and then query the database, as in a text retrieval problem. We show that such an approach, despite being based on state-of-the-art methods, is insufficient, and propose a method, where we do not rely on an exact localization and recognition pipeline. We take a query-driven search approach, where we find approximate locations of characters in the text query, and then impose spatial constraints to generate a ranked list of images in the database. The retrieval performance is evaluated on public scene text datasets as well as three large datasets, namely IIIT scene text retrieval, Sports-10K and TV series-1M, we introduce.
Fluttering Pattern Generation Using Modified Legendre Sequence for Coded Exposure Imaging
Authors: Hae-Gon Jeon, Joon-Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Finding a good binary sequence is critical in determining the performance of the coded exposure imaging, but previous methods mostly rely on a random search for finding the binary codes, which could easily fail to find good long sequences due to the exponentially growing search space. In this paper, we present a new computationally efficient algorithm for generating the binary sequence, which is especially well suited for longer sequences. We show that the concept of the low autocorrelation binary sequence that has been well exploited in the information theory community can be applied for generating the fluttering patterns of the shutter, propose a new measure of a good binary sequence, and present a new algorithm by modifying the Legendre sequence for the coded exposure imaging. Experiments using both synthetic and real data show that our new algorithm consistently generates better binary sequences for the coded exposure problem, yielding better deblurring and resolution enhancement results compared to the previous methods for generating the binary codes.
Prime Object Proposals with Randomized Prim's Algorithm
Authors: Santiago Manen, Matthieu Guillaumin, Luc Van Gool
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Generic object detection is the challenging task of proposing windows that localize all the objects in an image, regardless of their classes. Such detectors have recently been shown to benefit many applications such as speedingup class-specific object detection, weakly supervised learning of object detectors and object discovery. In this paper, we introduce a novel and very efficient method for generic object detection based on a randomized version of Prim's algorithm. Using the connectivity graph of an image's superpixels, with weights modelling the probability that neighbouring superpixels belong to the same object, the algorithm generates random partial spanning trees with large expected sum of edge weights. Object localizations are proposed as bounding-boxes of those partial trees. Our method has several benefits compared to the stateof-the-art. Thanks to the efficiency of Prim's algorithm, it samples proposals very quickly: 1000 proposals are obtained in about 0.7s. With proposals bound to superpixel boundaries yet diversified by randomization, it yields very high detection rates and windows that tightly fit objects. In extensive experiments on the challenging PASCAL VOC 2007 and 2012 and SUN2012 benchmark datasets, we show that our method improves over state-of-the-art competitors for a wide range of evaluation scenarios.
Optimization Problems for Fast AAM Fitting in-the-Wild
Authors: Georgios Tzimiropoulos, Maja Pantic
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We describe a very simple framework for deriving the most-well known optimization problems in Active Appearance Models (AAMs), and most importantly for providing efficient solutions. Our formulation results in two optimization problems for fast and exact AAM fitting, and one new algorithm which has the important advantage of being applicable to 3D. We show that the dominant cost for both forward and inverse algorithms is a few times mN which is the cost of projecting an image onto the appearance subspace. This makes both algorithms not only computationally realizable but also very attractive speed-wise for most current systems. Because exact AAM fitting is no longer computationally prohibitive, we trained AAMs in-the-wild with the goal of investigating whether AAMs benefit from such a training process. Our results show that although we did not use sophisticated shape priors, robust features or robust norms for improving performance, AAMs perform notably well and in some cases comparably with current state-ofthe-art methods. We provide Matlab source code for training, fitting and reproducing the results presented in this paper at http://ibug.doc.ic.ac.uk/resources.
Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization
Authors: Hua Wang, Feiping Nie, Weidong Cai, Heng Huang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Representing the raw input of a data set by a set of relevant codes is crucial to many computer vision applications. Due to the intrinsic sparse property of real-world data, dictionary learning, in which the linear decomposition of a data point uses a set of learned dictionary bases, i.e., codes, has demonstrated state-of-the-art performance. However, traditional dictionary learning methods suffer from three weaknesses: sensitivity to noisy and outlier samples, difficulty to determine the optimal dictionary size, and incapability to incorporate supervision information. In this paper, we address these weaknesses by learning a Semi-Supervised Robust Dictionary (SSR-D). Specifically, we use the l 2,0 + norm as the loss function to improve the robustness against outliers, and develop a new structured sparse regularization to incorporate the supervision information in dictionary learning, without incurring additional parameters. Moreover, the optimal dictionary size is automatically learned from the input data. Minimizing the derived objective function is challenging because it involves many non-smooth l 2,0 + -norm terms. We present an efficient algorithm to solve the problem with a rigorous proof of the convergence of the algorithm. Extensive experiments are presented to show the superior performance of the proposed method.