OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic 3D object recognition with both positive and negative evidences
Authors: Sukhan Lee 0001, Zhaojin Lu, Hyunwoo Kim
Venue: ICCV
Year: 2011

In real applications, sometimes, visual recognition may need to rely on incomplete or ambiguous features for a unique decision. Furthermore, the detected features may suffer a lot of uncertainties due to environment changes. In order to solve the problem with ambiguities and uncertainties in one computational framework, we propose a probabilistic 3D object recognition approach using both positive and negative evidences in cluttered environment. First of all, initial feature are selected as parallel and perpendicular line pairs to generate pose hypotheses as the multiple interpretations. Secondly, given a 3D polyhedral object model and the estimated pose, positive and negative evidences can be identified as additional information for probability computation of the multiple interpretations. More specifically, given the estimated pose, followed by visibility test, positive evidence is the feature that should be appeared around the pose, and negative evidence is the feature that should not be appeared due to self-occlusion. Where the probability is computed using Bayesian principle in terms of both likelihood and unlikelihood. The experimental results support the potential of the proposed approach in the real environment.
Handling label noise in video classification via multiple instance learning
Authors: Thomas Leung, Yang Song 0009, John Zhang
Venue: ICCV
Year: 2011

In many classification tasks, the use of expert-labeled data for training is often prohibitively expensive. The use of weakly-labeled data is an attractive solution but raises the problem of label noise. Multiple instance learning, whereby training samples are “bagged” instead of treated as singletons, offers a possible approach to mitigating the effects of label noise. In this paper, we propose the use of MILBoost [28] in a large-scale video taxonomic classification system comprised of hundreds of binary classifiers to handle noisy training data. We test on data with both artificial and real-world noise and compare against the state-of-the-art classifiers based on AdaBoost. We also explore the effects of different bag sizes on different levels of noise on the final classifier performance. Experiments show that when training classifiers with noisy data, MILBoost provides an improvement in performance.
Graph mode-based contextual kernels for robust SVM tracking
Authors: Xi Li 0001, Anthony R. Dick, Hanzi Wang, Chunhua Shen, Anton van den Hengel
Venue: ICCV
Year: 2011

Visual tracking has been typically solved as a binary classification problem. Most existing trackers only consider the pairwise interactions between samples, and thereby ignore the higher-order contextual interactions, which may lead to the sensitivity to complicated factors such as noises, outliers, background clutters and so on. In this paper, we propose a visual tracker based on support vector machines (SVMs), for which a novel graph mode-based contextual kernel is designed to effectively capture the higher-order contextual information from samples. To do so, we first create a visual graph whose similarity matrix is determined by a baseline visual kernel. Second, a set of high-order contexts are discovered in the visual graph. The problem of discovering these high-order contexts is solved by seeking modes of the visual graph. Each graph mode corresponds to a vertex community termed as a high-order context. Third, we construct a contextual kernel that effectively captures the interaction information between the high-order contexts. Finally, this contextual kernel is embedded into SVMs for robust tracking. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.
Optimal object matching via convexification and composition
Authors: Hongsheng Li 0001, Junzhou Huang, Shaoting Zhang 0001, Xiaolei Huang 0001
Venue: ICCV
Year: 2011

In this paper, we propose a novel object matching method to match an object to its instance in an input scene image, where both the object template and the input scene image are represented by groups of feature points. We relax each template point's discrete feature cost function to create a convex function that can be optimized efficiently. Such continuous and convex functions with different regularization terms are able to create different convex optimization models handling objects undergoing (i) global transformation, (ii) locally affine transformation, and (iii) articulated transformation. These models can better constrain each template point's transformation and therefore generate more robust matching results. Unlike traditional object or feature matching methods with “hard” node-to-node results, our proposed method allows template points to be transformed to any location in the image plane. Such a property makes our method robust to feature point occlusion or mis-detection. Our extensive experiments demonstrate the robustness and flexibility of our method.
Extracting adaptive contextual cues from unlabeled regions
Authors: Congcong Li, Devi Parikh, Tsuhan Chen
Venue: ICCV
Year: 2011

Existing approaches to contextual reasoning for enhanced object detection typically utilize other labeled categories in the images to provide contextual information. As a consequence, they inadvertently commit to the granularity of information implicit in the labels. Moreover, large portions of the images may not belong to any of the manually-chosen categories, and these unlabeled regions are typically neglected. In this paper, we overcome both these drawbacks and propose a contextual cue that exploits unlabeled regions in images. Our approach adaptively determines the granularity (scene, inter-object, intra-object, etc.) at which contextual information is captured. In order to extract the proposed contextual cue, we consider a scene to be a structured configuration of objects and regions; just as an object is a composition of parts. We thus learn our proposed “contextual meta-objects” using any off-the-shelf object detector, which makes our proposed cue widely accessible to the community. Our results show that incorporating our proposed cue provides a relative improvement of 12% over a state-of-the-art object detector on the challenging PASCAL dataset.
Treat samples differently: Object tracking with semi-supervised online CovBoost
Authors: Guorong Li, Lei Qin, Qingming Huang, Junbiao Pang, Shuqiang Jiang
Venue: ICCV
Year: 2011

Most feature selection methods for object tracking assume that the labeled samples obtained in the next frames follow the similar distribution with the samples in the previous frame. However, this assumption is not true in some scenarios. As a result, the selected features are not suitable for tracking and the “drift” problem happens. In this paper, we consider data's distribution in tracking from a new perspective. We classify the samples into three categories: auxiliary samples (samples in the previous frames), target samples (collected in the current frame) and unlabeled samples (obtained in the next frame). To make the best use of them for tracking, we propose a novel semi-supervised transfer learning approach. Specifically, we assume only target samples follow the same distribution as the unlabeled samples and develop a novel semi-supervised CovBoost method. It could utilize auxiliary samples and unlabeled samples effectively when training the best strong classifier for tracking. Furthermore, we develop a new online updating algorithm for semi-supervised CovBoost, making our tracker handle with significant variations of the tracked target and background successfully. We demonstrate the excellent performance of the proposed tracker on several challenging test videos.
Face recognition based on non-corresponding region matching
Authors: Annan Li, Shiguang Shan, Xilin Chen 0001, Wen Gao 0001
Venue: ICCV
Year: 2011

In previous works of face recognition, similarity between faces is measured by comparing corresponding face regions. That is to say, matching eyes with eyes and mouths with mouths etc.. In this paper, we propose that face can be also recognized by matching non-corresponding facial regions. In another word face can be recognized by matching eyes with mouths, for example. Specifically, the problem we study in this paper can be formulated as how to measure the possibility whether two non-corresponding face regions belong to the same face. We propose that the possibility can be measured via canonical correlation analysis. Experimental results show that it is feasible to recognize face via non-corresponding region matching. The proposed method provides an alternative and more flexible way to recognize faces.
Exploring regularized feature selection for person specific face verification
Authors: Yixiong Liang, Shenghui Liao, Lei Wang 0017, Beiji Zou 0001
Venue: ICCV
Year: 2011

In this paper, we explore the regularized feature selection method for person specific face verification in unconstrained environments. We reformulate the generalization of the single-task sparsity-enforced feature selection method to multi-task cases as a simultaneous sparse approximation problem. We also investigate two feature selection strategies in the multi-task generalization based on the positive and negative feature correlation assumptions across different persons. Simultaneous orthogonal matching pursuit (SOMP) is adopted and modified to solve the corresponding optimization problems. We further proposed a named simultaneous subspace pursuit (SSP) methods which generalize the subspace pursuit method to solve the corresponding optimization problems. The performance of different feature selection strategies and different solvers for face verification are compared on the challenging LFW face database. Our experimental results show that 1) the selected subsets based on positive correlation assumption are more effective than those based on the negative correlation assumption; 2) the OMP-based solvers outperform SP-based solvers in terms of feature selection and 3) the regularized methods with OMP-based solvers can outperform state-of-the-art feature selection methods.
Aerial 3D reconstruction with line-constrained dynamic programming
Authors: Huei-Hung Liao, Yuping Lin, Gérard G. Medioni
Venue: ICCV
Year: 2011

Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-image-column constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.
Pose estimation from reflections for specular surface recovery
Authors: Miaomiao Liu 0001, Kwan-Yee Kenneth Wong, Zhenwen Dai, Zhihu Chen
Venue: ICCV
Year: 2011

This paper addresses the problem of estimating the poses of a reference plane in specular shape recovery. Unlike existing methods which require an extra mirror or an extra reference plane and camera, our proposed method recovers the poses of the reference plane directly from its reflections on the specular surface. By establishing reflection correspondences on the reference plane in three distinct poses, our method estimates the poses of the reference plane in two steps. First, by applying a colinearity constraint to the reflection correspondences, a simple closed-form solution is derived for recovering the poses of the reference plane relative to its initial pose. Second, by applying a ray incidence constraint to the incident rays formed by the reflection correspondences and the visual rays cast from the image, a closed-form solution is derived for recovering the poses of the reference plane relative to the camera. The shape of the specular surface then follows. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our proposed method.
Latent Low-Rank Representation for subspace segmentation and feature extraction
Authors: Guangcan Liu, Shuicheng Yan
Venue: ICCV
Year: 2011

Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dictionary, which is a key aspect of LRR. However, such a strategy may depress the performance, especially when the observations are insufficient and/or grossly corrupted. In this paper we therefore propose to construct the dictionary by using both observed and unobserved, hidden data. We show that the effects of the hidden data can be approximately recovered by solving a nuclear norm minimization problem, which is convex and can be solved efficiently. The formulation of the proposed method, called Latent Low-Rank Representation (LatLRR), seamlessly integrates subspace segmentation and feature extraction into a unified framework, and thus provides us with a solution for both subspace segmentation and feature extraction. As a subspace segmentation algorithm, LatLRR is an enhanced version of LRR and outperforms the state-of-the-art algorithms. Being an unsupervised feature extraction algorithm, LatLRR is able to robustly extract salient features from corrupted data, and thus can work much better than the benchmark that utilizes the original data vectors as features for classification. Compared to dimension reduction based methods, LatLRR is more robust to noise.
Robust topological features for deformation invariant image matching
Authors: Edgar J. Lobaton, Ramanarayan Vasudevan, Ron Alterovitz, Ruzena Bajcsy
Venue: ICCV
Year: 2011

Local photometric descriptors are a crucial low level component of numerous computer vision algorithms. In practice, these descriptors are constructed to be invariant to a class of transformations. However, the development of a descriptor that is simultaneously robust to noise and invariant under general deformation has proven difficult. In this paper, we introduce the Topological-Attributed Relational Graph (T-ARG), a new local photometric descriptor constructed from homology that is provably invariant to locally bounded deformation. This new robust topological descriptor is backed by a formal mathematical framework. We apply T-ARG to a set of benchmark images to evaluate its performance. Results indicate that T-ARG significantly outperforms traditional descriptors for noisy, deforming images.
Spectral learning of latent semantics for action recognition
Authors: Zhiwu Lu 0001, Yuxin Peng, Horace Ho-Shing Ip
Venue: ICCV
Year: 2011

This paper proposes novel spectral methods for learning latent semantics (i.e. high-level features) from a large vocabulary of abundant mid-level features (i.e. visual keywords), which can help to bridge the semantic gap in the challenging task of action recognition. To discover the manifold structure hidden among mid-level features, we develop spectral embedding approaches based on graphs and hypergraphs, without the need to tune any parameter for graph construction which is a key step of manifold learning. In particular, the traditional graphs are constructed by linear reconstruction with sparse coding. In the new embedding space, we learn high-level latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our two spectral methods for semantic learning can discover the manifold structure hidden among mid-level features, which results in compact but discriminative high-level features. The experimental results on two standard action datasets have shown the superior performance of our spectral methods.
Discriminative multi-manifold analysis for face recognition from a single training sample per person
Authors: Jiwen Lu, Yap-Peng Tan, Gang Wang 0012
Venue: ICCV
Year: 2011

Conventional appearance-based face recognition methods usually assume there are multiple samples per person (MSPP) available during the training phase for discriminative feature extraction. In many practical face recognition applications such as law enhancement, e-passport and ID card identification, this assumption, however, may not hold as there is only a single sample per person (SSPP) enrolled or recorded in these systems. Many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning. To address this problem, we propose in this paper a novel discriminative multi-manifold analysis (DMMA) method by learning discriminative features from image patches. First, we partition each enrolled image into several non-overlapping patches to form an image set for each sample per person. Then, we formulate the SSPP face recognition as a manifold-manifold matching problem and learn multiple DMMA feature spaces to maximize the manifold margins of different persons. Lastly, we propose a reconstruction-based manifold-manifold distance to identify the unlabeled subjects. Experimental results on three widely used face databases are presented to demonstrate the efficacy of the proposed approach.
Discriminative high order SVD: Adaptive tensor subspace selection for image classification, clustering, and retrieval
Authors: Dijun Luo, Heng Huang 0001, Chris H. Q. Ding
Venue: ICCV
Year: 2011

Tensor based dimensionality reduction has recently attracted attention from computer vision and pattern recognition communities for both feature extraction and data compression. As an unsupervised method, High-Order Singular Value Decomposition (HOSVD) searches for low-rank subspaces such that the low-rank approximation error is minimized. In this paper, we propose a new unsupervised high-order tensor decomposition approach which employs the strength of discriminative analysis and K-means clustering to adaptively select subspaces that improve the clustering, classification, and retrieval capabilities of HOSVD. We provide both theoretical analysis to guarantee that our new method generates more discriminative subspaces and empirical studies on several public computer vision data sets to show the consistent improvement of our method over existing methods.
Linear dependency modeling for feature fusion
Authors: Andy Jinhua Ma, Pong Chi Yuen
Venue: ICCV
Year: 2011

This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution. In this paper, we prove that feature dependency can be modeled by a linear combination of the posterior probabilities under some mild assumptions. Based on the linear combination property, two methods, namely Linear Classifier Dependency Modeling (LCDM) and Linear Feature Dependency Modeling (LFDM), are derived and developed for dependency modeling in classifier level and feature level, respectively. The optimal models for LCDM and LFDM are learned by maximizing the margin between the genuine and imposter posterior probabilities. Both synthetic data and real datasets are used for experiments. Experimental results show that LFDM outperforms all existing combination methods.
Feature seeding for action recognition
Authors: Pyry Matikainen, Rahul Sukthankar, Martial Hebert
Venue: ICCV
Year: 2011

Progress in action recognition has been in large part due to advances in the features that drive learning-based methods. However, the relative sparsity of training data and the risk of overfitting have made it difficult to directly search for good features. In this paper we suggest using synthetic data to search for robust features that can more easily take advantage of limited data, rather than using the synthetic data directly as a substitute for real data. We demonstrate that the features discovered by our selection method, which we call seeding, improve performance on an action classification task on real data, even though the synthetic data from which the features are seeded differs significantly from the real data, both in terms of appearance and the set of action classes.
Temporally coded flash illumination for motion deblurring
Authors: Scott McCloskey
Venue: ICCV
Year: 2011

We use temporally sequenced flash illumination to capture coded exposure images of fast-moving objects in low light environments. These coded flash images allow for accurate estimation of blur-free latent images in the presence of object motion. By distributing flashes over a window of time, we lessen eye safety concerns associated with powerful all-at-once flashes. We show how our flash-based coded exposure system has better robustness to increasing object velocity than shutter-based exposure coding, thereby obviating the need for pre-exposure velocity estimation. We also show that the quality of the estimated sharp image is robust to varying levels of ambient illumination. This and other benefits of our coded flash system are demonstrated with real images acquired using prototype hardware.
Trajectory reconstruction from non-overlapping surveillance cameras with relative depth ordering constraints
Authors: Branislav Micusík
Venue: ICCV
Year: 2011

We present a method for reconstructing a trajectory of an object moving in front of non-overlapping fully or partially calibrated cameras. The non-overlapping setup turns that problem ill-posed as no point correspondences can be established which are necessary for the well known point triangulation. The proposed solution instead builds on the assumption of trajectory smoothness and depth ordering prior information. We propose a novel formulation with a consistent minimization criterion and a way to utilize the depth ordering prior reflected by the size change of a bounding box associated to an image point being tracked. Reconstructing trajectory minimizing the trajectory smoothness, its re-projection error and employing the depth priors is casted as the Second Order Cone Program yielding a global optimum. The new formulation together with the proposed depth prior significantly improves the trajectory reconstruction in sense of accuracy and topology, and speeds up the solver. Synthetic and real experiments validate the feasibility of the proposed approach.