OpenRaiser/Intern-Atlas huggingface dataset:
Center-Shift: An approach towards automatic robust mesh segmentation (ARMS)
Authors: Mengtian Sun, Yi Fang 0006, Karthik Ramani
Venue: CVPR
Year: 2012

In the area of 3D shape analysis, research in mesh segmentation has always been an important topic, as it is a fundamental low-level task which can be utilized in many applications including computer-aided design, computer animation, biomedical applications and many other fields. We define the automatic robust mesh segmentation (ARMS) method in this paper, which 1) is invariant to isometric transformation, 2) is insensitive to noise and deformation, 3) performs closely to human perception, 4) is efficient in computation, and 5) is minimally dependent on prior knowledge. In this work, we develop a new framework, namely the Center-Shift, which discovers meaningful segments of a 3D object by exploring the intrinsic geometric structure encoded in the biharmonic kernel. Our Center-Shift framework has three main steps: First, we construct a feature space where every vertex on the mesh surface is associated with the corresponding biharmonic kernel density function value. Second, we apply the Center-Shift algorithm for initial segmentation. Third, the initial segmentation result is refined through an efficient iterative process which leads to visually salient segmentation of the shape. The performance of this segmentation method is demonstrated through extensive experiments on various sets of 3D shapes and different types of noise and deformation. The experimental results of 3D shape segmentation have shown better performance of Center-Shift, compared to state-of-the-art segmentation methods.
Linear discriminative image processing operator analysis
Authors: Toru Tamaki, Bingzhi Yuan, Kengo Harada, Bisser Raytchev, Kazufumi Kaneda
Venue: CVPR
Year: 2012

In this paper, we propose a method to select a discriminative set of image processing operations for Linear Discriminant Analysis (LDA) as an application of the use of generating matrices representing image processing operators acting on images. First we show that generating matrices can be used for formulating LDA with increasing training samples, then analyze them as image processing operators acting on 2D continuous functions for compressing many large generating matrices by using PCA and Hermite decomposition. Then we propose Linear Discriminative Image Processing Operator Analysis, an iterative method for estimating LDA feature space along with a discriminative set of generating matrices. In experiments, we demonstrate that discriminative generating matrices outperform a non-discriminative set on the ORL and FERET datasets.
"Knock! Knock! Who is it?" probabilistic person identification in TV-series
Authors: Makarand Tapaswi, Martin Bäuml, Rainer Stiefelhagen
Venue: CVPR
Year: 2012

We describe a probabilistic method for identifying characters in TV series or movies. We aim at labeling every character appearance, and not only those where a face can be detected. Consequently, our basic unit of appearance is a person track (as opposed to a face track). We model each TV series episode as a Markov Random Field, integrating face recognition, clothing appearance, speaker recognition and contextual constraints in a probabilistic manner. The identification task is then formulated as an energy minimization problem. In order to identify tracks without faces, we learn clothing models by adapting available face recognition results. Within a scene, as indicated by prior analysis of the temporal structure of the TV series, clothing features are combined by agglomerative clustering. We evaluate our approach on the first 6 episodes of The Big Bang Theory and achieve an absolute improvement of 20% for person identification and 12% for face recognition.
Revisiting uncertainty in graph cut solutions
Authors: Daniel Tarlow, Ryan Prescott Adams
Venue: CVPR
Year: 2012

Graph cuts is a popular algorithm for finding the MAP assignment of many large-scale graphical models that are common in computer vision. While graph cuts is powerful, it does not provide information about the marginal probabilities associated with the solution it finds. To assess uncertainty, we are forced to fall back on less efficient and inexact inference algorithms such as loopy belief propagation, or use less principled surrogate representations of uncertainty such as the min-marginal approach of Kohli & Torr [8]. In this work, we give new justification for using min-marginals to compute the uncertainty in conditional random fields, framing the min-marginal outputs as exact marginals under a specially-chosen generative probabilistic model. We leverage this view to learn properly calibrated marginal probabilities as the result of straightforward maximization of the training likelihood, showing that the necessary subgradients can be computed efficiently using dynamic graph cut operations. We also show how this approach can be extended to compute multi-label marginal distributions, where again dynamic graph cuts enable efficient marginal inference and maximum likelihood learning. We demonstrate empirically that - after proper training - uncertainties based on min-marginals provide better-calibrated probabilities than baselines and that these distributions can be exploited in a decision-theoretic way for improved segmentation in low-level vision.
Iterative Nearest Neighbors for classification and dimensionality reduction
Authors: Radu Timofte, Luc Van Gool
Venue: CVPR
Year: 2012

Representative data in terms of a set of selected samples is of interest for various machine learning applications, e.g. dimensionality reduction and classification. The best-known techniques probably still are k-Nearest Neighbors (kNN) and its variants. Recently, richer representations have become popular. Examples are methods based on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized least squares (Sparse Representation (SR)) or l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -regularized least squares (Collaborative Representation (CR)), or on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -constrained least squares (Local Linear Embedding (LLE)). We propose Iterative Nearest Neighbors (INN). This is a novel sparse representation that combines the power of SR and LLE with the computational simplicity of kNN. We test our method in terms of dimensionality reduction and classification, using standard benchmarks such as faces (AR), traffic signs (GTSRB), and PASCAL VOC 2007. INN performs better than NN and comparable with CR and SR, while being orders of magnitude faster than the latter.
Joint motion estimation and segmentation of complex scenes with label costs and occlusion modeling
Authors: Markus Unger, Manuel Werlberger, Thomas Pock, Horst Bischof
Venue: CVPR
Year: 2012

We propose a unified variational formulation for joint motion estimation and segmentation with explicit occlusion handling. This is done by a multi-label representation of the flow field, where each label corresponds to a parametric representation of the motion. We use a convex formulation of the multi-label Potts model with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our formulation by means of convex constraints. Explicit occlusion handling eliminates errors otherwise created by the regularization. As occlusions can occur only at object boundaries, a large number of objects may be required. By using a fast primal-dual algorithm we are able to handle several hundred motion segments. Results are shown on several classical motion segmentation and optical flow examples.
Sparse kernel approximations for efficient classification and detection
Authors: Andrea Vedaldi, Andrew Zisserman
Venue: CVPR
Year: 2012

Efficient learning with non-linear kernels is often based on extracting features from the data that “linearise” the kernel. While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse features for arbitrary kernels, re-deriving as a special case a popular map for the intersection kernel and extending it to arbitrary additive kernels. We show that bundle optimisation methods can handle efficiently these sparse features in learning. As an application, we show that product quantisation can be interpreted as a sparse feature encoding, and use this to significantly accelerate learning with this technique. We demonstrate these ideas on image classification with Fisher kernels and object detection with deformable part models on the challenging PASCAL VOC data, obtaining five to ten-fold speed-ups as well as reducing memory use by an order of magnitude.
A tiered move-making algorithm for general pairwise MRFs
Authors: Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr
Venue: CVPR
Year: 2012

A large number of problems in computer vision can be modeled as energy minimization problems in a markov random field (MRF) framework. Many methods have been developed over the years for efficient inference, especially in pairwise MRFs. In general there is a trade-off between the complexity/efficiency of the algorithm and its convergence properties, with certain problems requiring more complex inference to handle general pairwise potentials. Graphcuts based α-expansion performs well on certain classes of energies, and sequential tree reweighted message passing (TRWS) and loopy belief propagation (LBP) can be used for non-submodular cases. These methods though suffer from poor convergence and often oscillate between solutions. In this paper, we propose a tiered move making algorithm which is an iterative method. Each move to the next configuration is based on the current labeling and an optimal tiered move, where each tiered move requires one application of the dynamic programming based tiered labeling method introduced in Felzenszwalb et. al. [2]. The algorithm converges to a local minimum for any general pairwise potential, and we give a theoretical analysis of the properties of the algorithm, characterizing the situations in which we can expect good performance. We evaluate the algorithm on many benchmark labeling problems such as stereo, image segmentation, image stitching and image denoising, as well as random energy minimization. Our method consistently gets better energy values than α-expansion, LBP, quadratic pseudo-boolean optimization (QPBO), and is competitive with TRWS.
A unifying resolution-independent formulation for early vision
Authors: Fabio Viola, Andrew W. Fitzgibbon, Roberto Cipolla
Venue: CVPR
Year: 2012

We present a model for early vision tasks such as denoising, super-resolution, deblurring, and demosaicing. The model provides a resolution-independent representation of discrete images which admits a truly rotationally invariant prior. The model generalizes several existing approaches: variational methods, finite element methods, and discrete random fields. The primary contribution is a novel energy functional which has not previously been written down, which combines the discrete measurements from pixels with a continuous-domain world viewed through continous-domain point-spread functions. The value of the functional is that simple priors (such as total variation and generalizations) on the continous-domain world become realistic priors on the sampled images. We show that despite its apparent complexity, optimization of this model depends on just a few computational primitives, which although tedious to derive, can now be reused in many domains. We define a set of optimization algorithms which greatly overcome the apparent complexity of this model, and make possible its practical application. New experimental results include infinite-resolution upsampling, and a method for obtaining “subpixel superpixels”.
A contextual maximum likelihood framework for modeling image registration
Authors: Christian Wachinger, Nassir Navab
Venue: CVPR
Year: 2012

We introduce a novel probabilistic framework for image registration. This framework considers, in contrast to previous ones, local neighborhood information. We integrate the neighborhood information into the framework by adding layers of latent random variables, characterizing the descriptive information of each image. This extension has multiple advantages. It allows for a unified description of geometric and iconic registration, with the consequential analysis of similarities. It enables to arrange registration techniques in a continuum, limited by pure intensity-and feature-based registration. With this wide spectrum of techniques combined, we can model hybrid registration approaches. The probabilistic coupling allows further to deduce optimal descriptors and to model the adaptation of description layers during the process, as it is done for joint registration/segmentation. Finally, we deduce a new registration algorithm that allows for a dynamic adaptation of the description layers during the registration. Excellent results confirm the advantages of the new registration method, the major contribution of this article lies, however, in the theoretical analysis.
Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease
Authors: Jing Wan, Zhilin Zhang 0002, Jingwen Yan, Taiyong Li, Bhaskar D. Rao, Shiaofen Fang, Sungeun Kim, Shannon L. Risacher, Andrew J. Saykin, Li Shen 0001
Venue: CVPR
Year: 2012

Alzheimer’s disease (AD) is the most common form of de-mentia that causes progressive impairment of memory and other cognitive functions. Multivariate regression models have been studied in AD for revealing relationships between neuroimaging measures and cognitive scores to understand how structural changes in brain can influence cognitive sta-tus. Existing regression methods, however, do not explic-itly model dependence relation among multiple scores de-rived from a single cognitive test. It has been found that such dependence can deteriorate the performance of these methods. To overcome this limitation, we propose an effi-cient sparse Bayesian multi-task learning algorithm, which adaptively learns and exploits the dependence to achieve improved prediction performance. The proposed algorithm is applied to a real world neuroimaging study in AD to pre-dict cognitive performance using MRI scans. The effective-ness of the proposed algorithm is demonstrated by its supe-rior prediction performance over multiple state-of-the-art competing methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are con-sistent with prior knowledge. 1.
What are good parts for hair shape modeling?
Authors: Nan Wang, Haizhou Ai, Feng Tang
Venue: CVPR
Year: 2012

Hair plays an important role in human appearance. However, hair segmentation is still a challenging problem partially due to the lack of an effective model to handle its arbitrary shape variations. In this paper, we present a part-based model robust to hair shape and environment variations. The key idea of our method is to identify local parts by promoting the effectiveness of the part-based model. To this end, we propose a measurable statistic, called Subspace Clustering Dependency (SC-Dependency), to estimate the co-occurrence probabilities between local shapes. SC-Dependency guarantees output reasonability and allows us to evaluate the effectiveness of part-wise constraints in an information-theoretic way. Then we formulate the part identification problem as an MRF that aims to optimize the effectiveness of the potential functions. Experiments are performed on a set of consumer images and show our algorithm's capability and robustness to handle hair shape variations and extreme environment conditions.
Detection by detections: Non-parametric detector adaptation for a video
Authors: Xiaoyu Wang 0002, Gang Hua 0001, Tony X. Han
Venue: CVPR
Year: 2012

We propose an approach to improving the detection results of a generic offline trained detector on a specific video. Our method does not leverage visual tracking as most detection by tracking methods do. Instead, the proposed detection by detections approach can serve as a more confident initialization for detection by tracking methods. Different from other supervised detector adaptation methods, we constrain the task to videos and no supervised labels for the target video are required for the adaptation; we intend to fill the gap between detection by tracking and pure detection by frames. As a non-parametric detector adaptation method, confident detections are collected to re-rank and to group other detections. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with two state-of-the-art detectors demonstrate the efficacy of our approach.
Multi-feature metric learning with knowledge transfer among semantics and social tagging
Authors: Shuhui Wang, Shuqiang Jiang, Qingming Huang, Qi Tian 0001
Venue: CVPR
Year: 2012

Previous metric learning approaches learn a unified metric for all the classes on single feature representation, thus cannot be directly transplanted to applications involving multiple features, hundreds to thousands of hierarchical structured semantics and abundant social tagging. In this paper, we propose a novel multi-task multi-feature metric learning method which models the information sharing mechanism among different learning tasks. We decompose the real world multi-class problems such as semantic categorization or automatic tagging into a set of tasks where each task corresponds to several classes with strong visual correlation. We conduct metric learning to learn a set of (hyper)category-specific metrics for all the tasks. By encouraging model sharing among tasks, more generalization power is acquired. Another advantage is the capability of simultaneous learning with semantic information and social tagging based on the multi-task learning framework, and thus they both benefit from the information provided by each other. Experiments demonstrate the advantages on applications including semantic categorization and automatic tagging compared with other popular metric learning approaches.
Learning sparse covariance patterns for natural scenes
Authors: Liwei Wang 0009, Yin Li 0003, Jiaya Jia, Jian Sun 0001, David P. Wipf, James M. Rehg
Venue: CVPR
Year: 2012

For scene classification, patch-level linear features do not always work as well as handcrafted features. In this paper, we present a new model to greatly improve the usefulness of linear features in classification by introducing co-variance patterns. We analyze their properties, discuss the fundamental importance, and present a generative model to properly utilize them. With this set of covariance information, in our framework, even the most naive linear features that originally lack the vital ability in classification become powerful. Experiments show that the performance of our new covariance model based on linear features is comparable with or even better than handcrafted features in scene classification.
Transferring a generic pedestrian detector towards specific scenes
Authors: Meng Wang, Wei Li 0044, Xiaogang Wang 0001
Venue: CVPR
Year: 2012

The performance of a generic pedestrian detector may drop significantly when it is applied to a specific scene due to mismatch between the source dataset used to train the detector and samples in the target scene. In this paper, we investigate how to automatically train a scene-specific pedestrian detector starting with a generic detector in video surveillance without further manually labeling any samples under a novel transfer learning framework. It tackles the problem from three aspects. (1) With a graphical representation and through exploring the indegrees from target samples to source samples, the source samples are properly re-weighted. The indegrees detect the boundary between the distributions of the source dataset and the target dataset. The re-weighted source dataset better matches the target scene. (2) It takes the context information from motions, scene structures and scene geometry as the confidence scores of samples from the target scene to guide transfer learning. (3) The confidence scores propagate among samples on a graph according to the underlying visual structures of samples. All these considerations are formulated under a single objective function called Confidence-Encoded SVM. At the test stage, only the appearance-based detector is used without the context cues. The effectiveness of the proposed framework is demonstrated through experiments on two video surveillance datasets. Compared with a generic pedestrian detector, it significantly improves the detection rate by 48% and 36% at one false positive per image on the two datasets respectively.
Affinity learning via self-diffusion for image segmentation and clustering
Authors: Bo Wang 0044, Zhuowen Tu
Venue: CVPR
Year: 2012

Computing a faithful affinity map is essential to the clustering and segmentation tasks. In this paper, we propose a graph-based affinity (metric) learning method and show its application to image clustering and segmentation. Our method, self-diffusion (SD), performs a diffusion process by propagating the similarity mass along the intrinsic manifold of data points. Theoretical analysis is given to the SD algorithm and we provide a way of deriving the critical time stamp t. Our method therefore has nearly no parameter tuning and leads to significantly improved affinity maps, which help to greatly enhance the quality of clustering. In addition, we show that much improved image segmentation results can be obtained by combining SD with e.g. the normalized cuts algorithm. The proposed method can be used to deliver robust affinity maps for a range of problems.
Action recognition by exploring data distribution and feature correlation
Authors: Sen Wang 0001, Yi Yang 0001, Zhigang Ma, Xue Li 0001, Chaoyi Pang, Alexander G. Hauptmann
Venue: CVPR
Year: 2012

Human action recognition in videos draws strong research interest in computer vision because of its promising applications for video surveillance, video annotation, interactive gaming, etc. However, the amount of video data containing human actions is increasing exponentially, which makes the management of these resources a challenging task. Given a database with huge volumes of unlabeled videos, it is prohibitive to manually assign specific action types to these videos. Considering that it is much easier to obtain a small number of labeled videos, a practical solution for organizing them is to build a mechanism which is able to conduct action annotation automatically by leveraging the limited labeled videos. Motivated by this intuition, we propose an automatic video annotation algorithm by integrating semi-supervised learning and shared structure analysis into a joint framework for human action recognition. We apply our algorithm on both synthetic and realistic video datasets, including KTH [20], CareMedia dataset [1], Youtube action [12] and its extended version, UCF50 [2]. Extensive experiments demonstrate that the proposed algorithm outperforms the compared algorithms for action recognition. Most notably, our method has a very distinct advantage over other compared algorithms when we have only a few labeled samples.