mtybilly/Instruct2DS huggingface dataset:
Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes
Authors: Joan Lasenby, Roberto Cipolla Sukrit Shankar
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks [19, 23]. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for realworld applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets.
Correlation Adaptive Subspace Segmentation by Trace Lasso
Authors: Jiashi Feng, Zhouchen Lin, Shuicheng Yan Canyi Lu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper studies the subspace segmentation problem. Given a set of data points drawn from a union of subspaces, the goal is to partition them into their underlying subspaces they were drawn from. The spectral clustering method is used as the framework. It requires to find an affinity matrix which is close to block diagonal, with nonzero entries corresponding to the data point pairs from the same subspace. In this work, we argue that both sparsity and the grouping effect are important for subspace segmentation. A sparse affinity matrix tends to be block diagonal, with less connections between data points from different subspaces. The grouping effect ensures that the highly corrected data which are usually from the same subspace can be grouped together. Sparse Subspace Clustering (SSC), by using dt-minimization, encourages sparsity for data selection, but it lacks of the grouping effect. On the contrary, Low-Rank Representation (LRR), by rank minimization, and Least Squares Regression (LSR), by nk-regularization, exhibit strong grouping effect, but they are short in subset selection. Thus the obtained affinity matrix is usually very sparse by SSC, yet very dense by LRR and LSR. In this work, we propose the Correlation Adaptive Subspace Segmentation (CASS) method by using trace Lasso. CASS is a data correlation dependent method which simultaneously performs automatic data selection and groups correlated data together. It can be regarded as a method which adaptively balances SSC and LSR. Both theoretical and experimental results show the effectiveness of CASS.
DCSH - Matching Patches in RGBD Images
Authors: Simon Korman, Eyal Ofek, Shai Avidan Yaron Eshet
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We extend patch based methods to work on patches in 3D space. We start with Coherency Sensitive Hashing [12] (CSH), which is an algorithm for matching patches between two RGB images, and extend it to work with RGBD images. This is done by warping all 3D patches to a common virtual plane in which CSH is performed. To avoid noise due to warping of patches of various normals and depths, we estimate a group of dominant planes and compute CSH on each plane separately, before merging the matching patches. The result is DCSH an algorithm that matches world (3D) patches in order to guide the search for image plane matches. An independent contribution is an extension of CSH, which we term Social-CSH. It allows a major speedup of the k nearest neighbor (kNN) version of CSH its runtime growing linearly, rather than quadratically, in k. Social-CSH is used as a subcomponent of DCSH when many NNs are required, as in the case of image denoising. We show the benefits of using depth information to image reconstruction and image denoising, demonstrated on several RGBD images.
Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos
Authors: Siwei Lyu, Bao-Gang Hu, Qiang Ji Baoyuan Wu
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We describe a novel method that simultaneously clusters and associates short sequences of detected faces (termed as face tracklets) in videos. The rationale of our method is that face tracklet clustering and linking are related problems that can benefit from the solutions of each other. Our method is based on a hidden Markov random field model that represents the joint dependencies of cluster labels and tracklet linking associations . We provide an efficient algorithm based on constrained clustering and optimal matching for the simultaneous inference of cluster labels and tracklet associations. We demonstrate significant improvements on the state-of-the-art results in face tracking and clustering performances on several video datasets.
Subpixel Scanning Invariant to Indirect Lighting Using Quadratic Code Length
Authors: Vincent Couture, Sebastien Roy Nicolas Martin
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a scanning method that recovers dense subpixel camera-projector correspondence without requiring any photometric calibration nor preliminary knowledge of their relative geometry. Subpixel accuracy is achieved by considering several zero-crossings defined by the difference between pairs of unstructured patterns. We use gray-level band-pass white noise patterns that increase robustness to indirect lighting and scene discontinuities. Simulated and experimental results show that our method recovers scene geometry with high subpixel precision, and that it can handle many challenges of active reconstruction systems. We compare our results to state of the art methods such as micro phase shifting and modulated phase shifting.
PM-Huber: PatchMatch with Huber Regularization for Stereo Matching
Authors: Sebastian Klose, Brian Jensen, Alois Knoll Philipp Heise
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Most stereo correspondence algorithms match support windows at integer-valued disparities and assume a constant disparity value within the support window. The recently proposed PatchMatch stereo algorithm [7] overcomes this limitation of previous algorithms by directly estimating planes. This work presents a method that integrates the PatchMatch stereo algorithm into a variational smoothing formulation using quadratic relaxation. The resulting algorithm allows the explicit regularization of the disparity and normal gradients using the estimated plane parameters. Evaluation of our method in the Middlebury benchmark shows that our method outperforms the traditional integer-valued disparity strategy as well as the original algorithm and its variants in sub-pixel accurate disparity estimation.
Relative Attributes for Large-Scale Abandoned Object Detection
Authors: Prasad Gabbur, Sharath Pankanti Quanfu Fan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Effective reduction of false alarms in large-scale video surveillance is rather challenging, especially for applications where abnormal events of interest rarely occur, such as abandoned object detection. We develop an approach to prioritize alerts by ranking them, and demonstrate its great effectiveness in reducing false positives while keeping good detection accuracy. Our approach benefits from a novel representation of abandoned object alerts by relative attributes, namely staticness, foregroundness and abandonment. The relative strengths of these attributes are quantified using a ranking function[19] learnt on suitably designed low-level spatial and temporal features.These attributes of varying strengths are not only powerful in distinguishing abandoned objects from false alarms such as people and light artifacts, but also computationally efficient for large-scale deployment. With these features, we apply a linear ranking algorithm to sort alerts according to their relevance to the end-user. We test the effectiveness of our approach on both public data sets and large ones collected from the real world.
Random Grids: Fast Approximate Nearest Neighbors and Range Searching for Image Search
Authors: Efi Kokiopoulou, Ehud Rivlin Dror Aiger
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose two solutions for both nearest neighbors and range search problems. For the nearest neighbors problem, we propose a c-approximate solution for the restricted version of the decision problem with bounded radius which is then reduced to the nearest neighbors by a known reduction. For range searching we propose a scheme that learns the parameters in a learning stage adopting them to the case of a set of points with low intrinsic dimension that are embedded in high dimensional space (common scenario for image point descriptors). We compare our algorithms to the best known methods for these problems, i.e. LSH, ANN and FLANN. We show analytically and experimentally that we can do better for moderate approximation factor. Our algorithms are trivial to parallelize. In the experiments conducted, running on couple of million images, our algorithms show meaningful speed-ups when compared with the above mentioned methods.
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation
Authors: Christian Reinbacher, Rene Ranftl, Matthias Ruether, Horst Bischof David Ferstl
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this work we present a novel method for the challenging problem of depth image upsampling. Modern depth cameras such as Kinect or Time of Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we formulate a convex optimization problem using higher order regularization for depth image upsampling. In this optimization an anisotropic diffusion tensor, calculated from a high resolution intensity image, is used to guide the upsampling. We derive a numerical algorithm based on a primaldual formulation that is efficiently parallelized and runs at multiple frames per second. We show that this novel upsampling clearly outperforms state of the art approaches in terms of speed and accuracy on the widely used Middlebury 2007 datasets. Furthermore, we introduce novel datasets with highly accurate groundtruth, which, for the first time, enable to benchmark depth upsampling methods using real sensor data.
3D Scene Understanding by Voxel-CRF
Authors: Pushmeet Kohli, Silvio Savarese Byung-Soo Kim
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Scene understanding is an important yet very challenging problem in computer vision. In the past few years, researchers have taken advantage of the recent diffusion of depth-RGB (RGB-D) cameras to help simplify the problem of inferring scene semantics. However, while the added 3D geometry is certainly useful to segment out objects with different depth values, it also adds complications in that the 3D geometry is often incorrect because of noisy depth measurements and the actual 3D extent of the objects is usually unknown because of occlusions. In this paper we propose a new method that allows us to jointly refine the 3D reconstruction of the scene (raw depth values) while accurately segmenting out the objects or scene elements from the 3D reconstruction. This is achieved by introducing a new model which we called Voxel-CRF. The Voxel-CRF model is based on the idea of constructing a conditional random field over a 3D volume of interest which captures the semantic and 3D geometric relationships among different elements (voxels) of the scene. Such model allows to jointly estimate (1) a dense voxel-based 3D reconstruction and (2) the semantic labels associated with each voxel even in presence of partial occlusions using an approximate yet efficient inference strategy. We evaluated our method on the challenging NYU Depth dataset (Version 1 and 2). Experimental results show that our method achieves competitive accuracy in inferring scene semantics and visually appealing results in improving the quality of the 3D reconstruction. We also demonstrate an interesting application of object removal and scene completion from RGB-D images.
No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion
Authors: Elisa Ricci, Ramanathan Subramanian, Oswald Lanz, Nicu Sebe Yan Yan
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose a novel Multi-Task Learning framework (FEGA-MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. As the target (person) moves, distortions in facial appearance owing to camera perspective and scale severely impede performance of traditional head pose classification methods. FEGA-MTL operates on a dense uniform spatial grid and learns appearance relationships across partitions as well as partition-specific appearance variations for a given head pose to build region-specific classifiers. Guided by two graphs which a-priori model appearance similarity among (i) grid partitions based on camera geometry and (ii) head pose classes, the learner efficiently clusters appearancewise related grid partitions to derive the optimal partitioning. For pose classification, upon determining the target's position using a person tracker, the appropriate regionspecific classifier is invoked. Experiments confirm that FEGA-MTL achieves state-of-the-art classification with few training data.
Dynamic Probabilistic Volumetric Models
Authors: Octavian Biris, Joseph L. Mundy Ali Osman Ulusoy
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step and do not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces. This representation is shown to achieve compression of 4-d data and provide efficient spatio-temporal processing. The advances of the proposed framework is demonstrated on standard datasets using free-viewpoint video and 3-d tracking applications.
Predicting an Object Location Using a Global Image Representation
Authors: Diane Larlus Jose A. Rodriguez Serrano
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We tackle the detection of prominent objects in images as a retrieval task: given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data driven detection (DDD), that is an alternative to sliding windows. Previous works have used similar notions but with task-independent similarities and representations, i.e. they were not tailored to the end-goal of localization. This article proposes two contributions: (i) a metric learning algorithm and (ii) a representation of images as object probability maps, that are both optimized for detection. We show experimentally that these two contributions are crucial to DDD, do not require costly additional operations, and in some cases yield comparable or better results than state-of-the-art detectors despite conceptual simplicity and increased speed. As an application of prominent object detection, we improve fine-grained categorization by precropping images with the proposed approach.
Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
Authors: Vincent De Smet, Luc Van Gool Radu Timofte
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Recently there have been significant advances in image upscaling or image super-resolution based on a dictionary of low and high resolution exemplars. The running time of the methods is often ignored despite the fact that it is a critical factor for real applications. This paper proposes fast super-resolution methods while making no compromise on quality. First, we support the use of sparse learned dictionaries in combination with neighbor embedding methods. In this case, the nearest neighbors are computed using the correlation with the dictionary atoms rather than the Euclidean distance. Moreover, we show that most of the current approaches reach top performance for the right parameters. Second, we show that using global collaborative coding has considerable speed advantages, reducing the super-resolution mapping to a precomputed projective matrix. Third, we propose the anchored neighborhood regression. That is to anchor the neighborhood embedding of a low resolution patch to the nearest atom in the dictionary and to precompute the corresponding embedding matrix. These proposals are contrasted with current state-ofthe-art methods on standard images. We obtain similar or improved quality and one or two orders of magnitude speed improvements.
Robust Object Tracking with Online Multi-lifespan Dictionary Learning
Authors: Jin Gao, Bing Li, Weiming Hu, Shuicheng Yan Junliang Xing
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Recently, sparse representation has been introduced for robust object tracking. By representing the object sparsely, i.e., using only a few templates via 1 -norm minimization, these so-called 1 -trackers exhibit promising tracking results. In this work, we address the object template building and updating problem in these 1 -tracking approaches, which has not been fully studied. We propose to perform template updating, in a new perspective, as an online incremental dictionary learning problem, which is efficiently solved through an online optimization procedure. To guarantee the robustness and adaptability of the tracking algorithm, we also propose to build a multi-lifespan dictionary model. By building target dictionaries of different lifespans, effective object observations can be obtained to deal with the well-known drifting problem in tracking and thus improve the tracking accuracy. We derive effective observation models both generatively and discriminatively based on the online multi-lifespan dictionary learning model and deploy them to the Bayesian sequential estimation framework to perform tracking. The proposed approach has been extensively evaluated on ten challenging video sequences. Experimental results demonstrate the effectiveness of the online learned templates, as well as the state-of-the-art tracking performance of the proposed approach.
Finding the Best from the Second Bests - Inhibiting Subjective Bias in Evaluation of Visual Tracking Algorithms
Authors: Haibin Ling Yu Pang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Evaluating visual tracking algorithms, or "trackers" for short, is of great importance in computer vision. However, it is hard to "fairly" compare trackers due to many parameters need to be tuned in the experimental configurations. On the other hand, when introducing a new tracker, a recent trend is to validate it by comparing it with several existing ones. Such an evaluation may have subjective biases towards the new tracker which typically performs the best. This is mainly due to the difficulty to optimally tune all its competitors and sometimes the selected testing sequences. By contrast, little subjective bias exists towards the "second best" ones 1 in the contest. This observation inspires us with a novel perspective towards inhibiting subjective bias in evaluating trackers by analyzing the results between the second bests. In particular, we first collect all tracking papers published in major computer vision venues in recent years. From these papers, after filtering out potential biases in various aspects, we create a dataset containing many records of comparison results between various visual trackers. Using these records, we derive performance rankings of the involved trackers by four different methods. The first two methods model the dataset as a graph and then derive the rankings over the graph, one by a rank aggregation algorithm and the other by a PageRank-like solution. The other two methods take the records as generated from sports contests and adopt widely used Elo's and Glicko's rating systems to derive the rankings. The experimental results are presented and may serve as a reference for related research.
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions
Authors: Babak Saleh, Ahmed Elgammal Mohamed Elhoseiny
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The main question we address in this paper is how to use purely textual description of categories with no training images to learn visual classifiers for these categories. We propose an approach for zero-shot learning of object categories where the description of unseen categories comes in the form of typical text such as an encyclopedia entry, without the need to explicitly defined attributes. We propose and investigate two baseline formulations, based on regression and domain adaptation. Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the classifier parameters for new classes. We applied the proposed approach on two fine-grained categorization datasets, and the results indicate successful classifier prediction.
Detecting Dynamic Objects with Multi-view Background Subtraction
Authors: Sam Hallman, Charless C. Fowlkes Raul Diaz
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The confluence of robust algorithms for structure from motion along with high-coverage mapping and imaging of the world around us suggests that it will soon be feasible to accurately estimate camera pose for a large class photographs taken in outdoor, urban environments. In this paper, we investigate how such information can be used to improve the detection of dynamic objects such as pedestrians and cars. First, we show that when rough camera location is known, we can utilize detectors that have been trained with a scene-specific background model in order to improve detection accuracy. Second, when precise camera pose is available, dense matching to a database of existing images using multi-view stereo provides a way to eliminate static backgrounds such as building facades, akin to background-subtraction often used in video analysis. We evaluate these ideas using a dataset of tourist photos with estimated camera pose. For template-based pedestrian detection, we achieve a 50 percent boost in average precision over baseline.
Face Recognition via Archetype Hull Ranking
Authors: Wei Liu, Deli Zhao, Xiaoou Tang Yuanjun Xiong
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: The archetype hull model is playing an important role in large-scale data analytics and mining, but rarely applied to vision problems. In this paper, we migrate such a geometric model to address face recognition and verification together through proposing a unified archetype hull ranking framework. Upon a scalable graph characterized by a compact set of archetype exemplars whose convex hull encompasses most of the training images, the proposed framework explicitly captures the relevance between any query and the stored archetypes, yielding a rank vector over the archetype hull. The archetype hull ranking is then executed on every block of face images to generate a blockwise similarity measure that is achieved by comparing two different rank vectors with respect to the same archetype hull. After integrating blockwise similarity measurements with learned importance weights, we accomplish a sensible face similarity measure which can support robust and effective face recognition and verification. We evaluate the face similarity measure in terms of experiments performed on three benchmark face databases Multi-PIE, Pubfig83, and LFW, demonstrating its performance superior to the state-of-the-arts.
Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach
Authors: Kevin Cannons, Greg Mori, Sangmin Oh, Ilseo Kim Arash Vahdat
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We present a compositional model for video event detection. A video is modeled using a collection of both global and segment-level features and kernel functions are employed for similarity comparisons. The locations of salient, discriminative video segments are treated as a latent variable, allowing the model to explicitly ignore portions of the video that are unimportant for classification. A novel, multiple kernel learning (MKL) latent support vector machine (SVM) is defined, that is used to combine and re-weight multiple feature types in a principled fashion while simultaneously operating within the latent variable framework. The compositional nature of the proposed model allows it to respond directly to the challenges of temporal clutter and intra-class variation, which are prevalent in unconstrained internet videos. Experimental results on the TRECVID Multimedia Event Detection 2011 (MED11) dataset demonstrate the efficacy of the method.
Nested Shape Descriptors
Authors: Jianbo Shi Jeffrey Byrne
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a new family of binary local feature descriptors called nested shape descriptors. These descriptors are constructed by pooling oriented gradients over a large geometric structure called the Hawaiian earring, which is constructed with a nested correlation structure that enables a new robust local distance function called the nesting distance. This distance function is unique to the nested descriptor and provides robustness to outliers from order statistics. In this paper, we define the nested shape descriptor family and introduce a specific member called the seed-of-life descriptor. We perform a trade study to determine optimal descriptor parameters for the task of image matching. Finally, we evaluate performance compared to state-of-the-art local feature descriptors on the VGGAffine image matching benchmark, showing significant performance gains. Our descriptor is the first binary descriptor to outperform SIFT on this benchmark.
Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees
Authors: Shuanak Chatterjee, Rene Vidal Aastha Jain
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose an exact, general and efficient coarse-to-fine energy minimization strategy for semantic video segmentation. Our strategy is based on a hierarchical abstraction of the supervoxel graph that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies defined over coarser graphs. The strategy is exact, i.e., it produces the same solution as minimizing over the finest graph. It is general, i.e., it can be used to minimize any energy function (e.g., unary, pairwise, and higher-order terms) with any existing energy minimization algorithm (e.g., graph cuts and belief propagation). It also gives significant speedups in inference for several datasets with varying degrees of spatio-temporal continuity. We also discuss the strengths and weaknesses of our strategy relative to existing hierarchical approaches, and the kinds of image and video data that provide the best speedups.
Local Signal Equalization for Correspondence Matching
Authors: Thabo Beeler Derek Bradley
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Correspondence matching is one of the most common problems in computer vision, and it is often solved using photo-consistency of local regions. These approaches typically assume that the frequency content in the local region is consistent in the image pair, such that matching is performed on similar signals. However, in many practical situations this is not the case, for example with low depth of field cameras a scene point may be out of focus in one view and in-focus in the other, causing a mismatch of frequency signals. Furthermore, this mismatch can vary spatially over the entire image. In this paper we propose a local signal equalization approach for correspondence matching. Using a measure of local image frequency, we equalize local signals using an efficient scale-space image representation such that their frequency contents are optimally suited for matching. Our approach allows better correspondence matching, which we demonstrate with a number of stereo reconstruction examples on synthetic and real datasets.
On One-Shot Similarity Kernels: Explicit Feature Maps and Properties
Authors: Irene Kotsia Stefanos Zafeiriou
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: Kernels have been a common tool of machine learning and computer vision applications for modeling nonlinearities and/or the design of robust 1 similarity measures between objects. Arguably, the class of positive semidefinite (psd) kernels, widely known as Mercer’s Kernels,constitutes one of the most well-studied cases. For every psd kernel there exists an associated feature map to an arbitrary dimensional Hilbert space H, the so-called feature space. The main reason behind psd kernels’ popularity is the fact that classification/regression techniques (such as Support Vector Machines (SVMs)) and component analysis algorithms (such as Kernel Principal Component Analysis (KPCA)) can be devised in H, without an explicit definition of the feature map, only by using the kernel (the so-called kernel trick). Recently, due to the development of very efficient solutions for large scale linear SVMs and for incremental linear component analysis, the research towards finding feature map approximations for classes of kernels has attracted significant interest. In this paper, we attempt the derivation of explicit feature maps of a recently proposed class of kernels, the so-called one-shot similarity kernels. We show that for this class of kernels either there exists an explicit representation in feature space or the kernel can be expressed in such a form that allows for exact incremental learning. We theoretically explore the properties of these kernels and show how these kernels can be used for the development of robust visual tracking, recognition and deformable fitting algorithms.
Combining the Right Features for Complex Event Recognition
Authors: Bangpeng Yao, Li Fei-Fei, Daphne Koller Kevin Tang
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: In this paper, we tackle the problem of combining features extracted from video for complex event recognition. Feature combination is an especially relevant task in video data, as there are many features we can extract, ranging from image features computed from individual frames to video features that take temporal information into account. To combine features effectively, we propose a method that is able to be selective of different subsets of features, as some features or feature combinations may be uninformative for certain classes. We introduce a hierarchical method for combining features based on the AND/OR graph structure, where nodes in the graph represent combinations of different sets of features. Our method automatically learns the structure of the AND/OR graph using score-based structure learning, and we introduce an inference procedure that is able to efficiently compute structure scores. We present promising results and analysis on the difficult and large-scale 2011 TRECVID Multimedia Event Detection dataset [17].
NEIL: Extracting Visual Knowledge from Web Data
Authors: Abhinav Shrivastava, Abhinav Gupta Xinlei Chen
Published in: IEEE International Conference on Computer Vision (ICCV), 2013 (Main Conference Track), 2013

Abstract: We propose NEIL (Never Ending Image Learner), a computer program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from Internet data. NEIL uses a semi-supervised learning algorithm that jointly discovers common sense relationships (e.g., "Corolla is a kind of/looks similar to Car","Wheel is a part of Car") and labels instances of the given visual categories. It is an attempt to develop the world's largest visual structured knowledge base with minimum human labeling effort. As of 10 th October 2013, NEIL has been continuously running for 2.5 months on 200 core cluster (more than 350K CPU hours) and has an ontology of 1152 object categories, 1034 scene categories and 87 attributes. During this period, NEIL has discovered more than 1700 relationships and has labeled more than 400K visual instances.
Joint Subspace Stabilization for Stereoscopic Video
Authors: Yuzhen Niu, Hailin Jin Feng Liu
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: Jingdong Wang, Ping Tan, Jinglu Wang, Long Quan Honghui Zhang
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: Sanja Fidler, Marc Pollefeys, Raquel Urtasun Alexander G. Schwing
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: Zhaopeng Cui, Ping Tan Nianjuan Jiang
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: Feiping Nie, Weidong Cai, Heng Huang Xiao Cai
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: Martial Hebert Scott Satkin
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: Jinhui Tang, Min Lin, Liang Lin, Shuicheng Yan, Zhouchen Lin Canyi Lu
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: Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann Mahsa Baktashmotlagh
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: Christian Kerl, Daniel Cremers Frank Steinbrucker
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: Sanja Fidler, Sven Dickinson Tom Sie Ho Lee
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: Srinivasa G. Narasimhan Yuandong Tian
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.