OpenRaiser/Intern-Atlas huggingface dataset:
Vector array based Multi-View Face Detection with compound exemplars
Authors: Kai Ma, Jezekiel Ben-Arie
Venue: CVPR
Year: 2012

We address the problem of Multiple View Face Detection (MVFD) in unconstrained environments. In order to achieve generalized face detection we use part-based image representations by tessellation of small image patches, which are typified by 2D vector arrays. Faces are detected by a method named Vector Array Recognition by Indexing and Sequencing (VARIS). VARIS is designed to find the optimal similarity matching between the input image and stored exemplars while allowing wide geometrical variations that are limited only by topological constraints. Aggregated similarity is further enhanced by matching the input images with compound exemplars. The novel compounding procedure also reduces the number of exemplars necessary for each class representation. VARIS with compounding performs efficient parallel classification and has polynomial computational complexity.
Sparse representation for face recognition based on discriminative low-rank dictionary learning
Authors: Long Ma, Chunheng Wang, Baihua Xiao, Wen Zhou 0002
Venue: CVPR
Year: 2012

In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.
A regularized spectral algorithm for Hidden Markov Models with applications in computer vision
Authors: Ha Quang Minh, Marco Cristani, Alessandro Perina, Vittorio Murino
Venue: CVPR
Year: 2012

Hidden Markov Models (HMMs) are among the most important and widely used techniques to deal with sequential or temporal data. Their application in computer vision ranges from action/gesture recognition to videosurveillance through shape analysis. Although HMMs are often embedded in complex frameworks, this paper focuses on theoretical aspects of HMM learning. We propose a regularized algorithm for learning HMMs in the spectral framework, whose computations have no local minima. Compared with recently proposed spectral algorithms for HMMs, our method is guaranteed to produce probability values which are always physically meaningful and which, on synthetic mathematical models, give very good approximations to true probability values. Furthermore, we place no restriction on the number of symbols and the number of states. On various pattern recognition data sets, our algorithm consistently outperforms classical HMMs, both in accuracy and computational speed. This and the fact that HMMs are used in vision as building blocks for more powerful classification approaches, such as generative embedding approaches or more complex generative models, strongly support spectral HMMs (SHMMs) as a new basic tool for pattern recognition.
Foreground detection using spatiotemporal projection kernels
Authors: Yair Moshe, Hagit Hel-Or, Yacov Hel-Or
Venue: CVPR
Year: 2012

In this paper, we propose a novel video foreground detection method that exploits the statistics of 3D spacetime patches. 3D space-time patches are characterized by means of the subspace they span. As the complexity of real-time systems prohibits performing this modeling directly on the raw pixel data, we propose a novel framework in which spatiotemporal data is sequentially reduced in two stages. The first stage reduces the data using a cascade of linear projections of 3D space-time patches onto a small set of 3D Walsh-Hadamard (WH) basis functions known for its energy compaction of natural images and videos. This stage is efficiently implemented using the Gray-Code filtering scheme [2] requiring only 2 operations per projection. In the second stage, the data is further reduced by applying PCA directly to the WH coefficients exploiting the local statistics in an adaptive manner. Unlike common techniques, this spatiotemporal adaptive projection exploits window appearance as well as its dynamic characteristics. Tests show that the proposed method outperforms recent foreground detection methods and is suitable for real-time implementation on streaming video.
Feature-domain super-resolution framework for Gabor-based face and iris recognition
Authors: Kien Nguyen Thanh, Sridha Sridharan, Simon Denman, Clinton Fookes
Venue: CVPR
Year: 2012

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics.
Max-margin early event detectors
Authors: Minh Hoai Nguyen, Fernando De la Torre
Venue: CVPR
Year: 2012

The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach. To the best of our knowledge, this is the first paper in the literature of computer vision that proposes a learning formulation for early event detection.
Omni-range spatial contexts for visual classification
Authors: Bingbing Ni, Mengdi Xu, Jinhui Tang 0001, Shuicheng Yan, Pierre Moulin
Venue: CVPR
Year: 2012

Spatial contexts encode rich discriminative information for visual classification. However, as object shapes and scales vary significantly among images, spatial contexts with manually specified distance ranges are not guaranteed with optimality. In this work, we investigate how to automatically select discriminative and stable distance bin groups for modeling image spatial contexts to improve classification performance. We make two observations. First, the number of distance bins for context modeling can be arbitrarily large, and discriminative contexts are only from a small subset of distance bins. Second, adjacent distance bins for contexts modeling often show similar characteristics, thus encouraging grouping them together can result in more stable representation. Utilizing these two observations, we propose an omni-range spatial context mining framework for image classification. A sparse selection and grouping regularizer is employed along with an empirical risk, to discover discriminative and stable distance bin groups for context modeling. To facilitate efficient optimization, the objective function is approximated by a smooth convex function with theoretically guaranteed error bounds. The selected and grouped image spatial contexts, which are applied in food and national flag recognition, are demonstrated to be discriminative, compact and robust.
Fast search in Hamming space with multi-index hashing
Authors: Mohammad Norouzi 0002, Ali Punjani, David J. Fleet
Venue: CVPR
Year: 2012

There has been growing interest in mapping image data onto compact binary codes for fast near neighbor search in vision applications. Although binary codes are motivated by their use as direct indices (addresses) into a hash table, codes longer than 32 bits are not being used in this way, as it was thought to be ineffective. We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact K-nearest neighbor search in Hamming space. The algorithm is straightforward to implement, storage efficient, and it has sub-linear run-time behavior for uniformly distributed codes. Empirical results show dramatic speed-ups over a linear scan baseline and for datasets with up to one billion items, 64- or 128-bit codes, and search radii up to 25 bits.
Optimal integration of photometric and geometric surface measurements using inaccurate reflectance/illumination knowledge
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: CVPR
Year: 2012

In this paper, we present a method for accurately estimating the shape of an object by integrating the surface orientation measured by photometric stereo and the position measured by some range-measuring method. We first show that even if the knowledge of the reflectance/illumination is inaccurate, the first derivatives of the photometrically measured orientation can be accurately estimated at the surface points where they have small values. We propose a probabilistic framework to quantitate the (in)accuracy of the knowledge and connect it to the estimation accuracy of these derivatives. Based on this framework, we consider optimally integrating the surface orientation and position to obtain the object shape with higher accuracy. The integration reduces to an optimization problem, and it is efficiently solved by belief propagation. We present several experimental results showing the effectiveness of the proposed approach.
Locally Orderless Tracking
Authors: Shaul Oron, Aharon Bar-Hillel, Dan Levi, Shai Avidan
Venue: CVPR
Year: 2012

Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the object. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the object variations over time. The model is implemented using the Earth Mover's Distance (EMD) with two parameters that control the cost of moving pixels and changing their color. We adjust these costs on-line during tracking to account for the amount of local (dis)order in the object. We show LOT's tracking capabilities on challenging video sequences, both commonly used and new, demonstrating performance comparable to state-of-the-art methods.
Sharing features in multi-class boosting via group sparsity
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel
Venue: CVPR
Year: 2012

We present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features. Our multi-class boosting is solved in a single optimization problem. In order to share features across different classes, we introduce the mixed-norm regularization, which promotes group sparsity, into boosting. We then derive the Lagrange dual problems which enable us to design fully corrective multi-class algorithms using the primal-dual optimization technique. We show that sharing features across classes can improve classification performance and efficiency. We empirically show that in many cases, the proposed multi-class boosting generalizes better than a range of competing multi-class boosting algorithms due to the capability of feature sharing. Experimental results on machine learning data, visual scene and object recognition demonstrate the efficiency and effectiveness of proposed algorithms and validate our theoretical findings.
Graph-based detection, segmentation & characterization of brain tumors
Authors: Sarah Parisot, Hugues Duffau, Stéphane Chemouny, Nikos Paragios
Venue: CVPR
Year: 2012

In this paper we propose a novel approach for detection, segmentation and characterization of brain tumors. Our method exploits prior knowledge in the form of a sparse graph representing the expected spatial positions of tumor classes. Such information is coupled with image-based classification techniques along with spatial smoothness constraints towards producing a reliable detection map within a unified graphical model formulation. Towards optimal use of prior knowledge, a two layer interconnected graph is considered with one layer corresponding to the low-grade glioma type (characterization) and the second layer to voxel-based decisions of tumor presence. Efficient linear programming both in terms of performance as well as in terms of computational load is considered to recover the lowest potential of the objective function. The outcome of the method refers to both tumor segmentation as well as their characterization. Promising results on substantial data sets demonstrate the extreme potentials of our method.
Cats and dogs
Authors: Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, C. V. Jawahar
Venue: CVPR
Year: 2012

We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem.
Teaching 3D geometry to deformable part models
Authors: Bojan Pepik, Michael Stark 0003, Peter V. Gehler, Bernt Schiele
Venue: CVPR
Year: 2012

Current object class recognition systems typically target 2D bounding box localization, encouraged by benchmark data sets, such as Pascal VOC. While this seems suitable for the detection of individual objects, higher-level applications such as 3D scene understanding or 3D object tracking would benefit from more fine-grained object hypotheses incorporating 3D geometric information, such as viewpoints or the locations of individual parts. In this paper, we help narrowing the representational gap between the ideal input of a scene understanding system and object class detector output, by designing a detector particularly tailored towards 3D geometric reasoning. In particular, we extend the successful discriminatively trained deformable part models to include both estimates of viewpoint and 3D parts that are consistent across viewpoints. We experimentally verify that adding 3D geometric information comes at minimal performance loss w.r.t. 2D bounding box localization, but outperforms prior work in 3D viewpoint estimation and ultra-wide baseline matching.
Saliency filters: Contrast based filtering for salient region detection
Authors: Federico Perazzi, Philipp Krähenbühl, Yael Pritch, Alexander Hornung
Venue: CVPR
Year: 2012

Saliency estimation has become a valuable tool in image processing. Yet, existing approaches exhibit considerable variation in methodology, and it is often difficult to attribute improvements in result quality to specific algorithm properties. In this paper we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation. Our algorithm consists of four basic steps. First, our method decomposes a given image into compact, perceptually homogeneous elements that abstract unnecessary detail. Based on this abstraction we compute two measures of contrast that rate the uniqueness and the spatial distribution of these elements. From the element contrast we then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore- and background. We show that the complete contrast and saliency estimation can be formulated in a unified way using high-dimensional Gaussian filters. This contributes to the conceptual simplicity of our method and lends itself to a highly efficient implementation with linear complexity. In a detailed experimental evaluation we analyze the contribution of each individual feature and show that our method outperforms all state-of-the-art approaches.
The Random Cluster Model for robust geometric fitting
Authors: Trung-Thanh Pham, Tat-Jun Chin, Jin Yu 0001, David Suter
Venue: CVPR
Year: 2012

Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal or elemental subsets of the data, and hypothesize the geometric model from the selected subsets. While taking minimal subsets increases the chance of simultaneously “hitting” inliers in a sample, it amplifies the noise of the underlying model, and hypotheses fitted on minimal subsets may be severely biased even if they contain purely inliers. In this paper we propose to use Random Cluster Models, a technique used to simulate coupled spin systems, to conduct hypothesis generation using subsets larger than minimal. We show how large clusters of data from genuine instances of the geometric model can be efficiently harvested to produce more accurate hypotheses. To take advantage of our hypothesis generator, we construct a simple annealing method based on graph cuts to fit multiple instances of the geometric model in the data. Experimental results show clear improvements in efficiency over other methods based on minimal subset samplers.
Improved subspace clustering via exploitation of spatial constraints
Authors: Duc-Son Pham 0001, Budhaditya Saha, Dinh Q. Phung, Svetha Venkatesh
Venue: CVPR
Year: 2012

We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation.
Steerable part models
Authors: Hamed Pirsiavash, Deva Ramanan
Venue: CVPR
Year: 2012

We describe a method for learning steerable deformable part models. Our models exploit the fact that part templates can be written as linear filter banks. We demonstrate that one can enforce steerability and separability during learning by applying rank constraints. These constraints are enforced with a coordinate descent learning algorithm, where each step can be solved with an off-the-shelf structured SVM solver. The resulting models are orders of magnitude smaller than their counterparts, greatly simplifying learning and reducing run-time computation. Limiting the degrees of freedom also reduces overfitting, which is useful for learning large part vocabularies from limited training data. We learn steerable variants of several state-of-the-art models for object detection, human pose estimation, and facial landmark estimation. Our steerable models are smaller, faster, and often improve performance.
Customizing biometric authentication systems via discriminative score calibration
Authors: Norman Poh, Massimo Tistarelli
Venue: CVPR
Year: 2012

There is mounting evidence about the benefit of tailoring a biometric authentication system to each user by postprocessing the system output at the score level, also known as client-specific score normalisation. Examples of these procedures are Z-norm and F-norm. These procedures can calibrate the uneven hypothesis space such that the dispropotionate false acceptance and false rejection errors are reduced after the calibration. The interest in studying these schemes is that they are applicable to any biometric authentication system regardless of the underlying biometric modality, and furthermore, potentially be extended to object recognition framed as a verification problem. We propose to further improve these procedures by adding additional client-specific terms that cannot be incorporated easily in their respective existing form. Experiments carried out on 13 face and speech systems show that both variants systematically outperform their respective score normalisation scheme (Z-norm or F-norm).
SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition
Authors: Carolina Redondo-Cabrera, Roberto Javier López-Sastre, Javier Acevedo-Rodríguez, Saturnino Maldonado-Bascón
Venue: CVPR
Year: 2012

This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the point clouds, a vocabulary of 3D visual words is generated. Using this codebook, we build a Bag-of-Words representation in 3D, which is used in conjunction with a SVM classification machinery. We also introduce the 3D Spatial Pyramid Matching Kernel, which works by partitioning a working volume into fine sub-volumes, and computing a hierarchical weighted sum of histogram intersections at each level of the pyramid structure. With the aim of increasing both the classification accuracy and the computational efficiency of the kernel, we propose selective hierarchical volume decomposition strategies, based on representative and discriminative (sub-)volume selection processes, which drastically reduce the pyramid to consider. Results on the challenging large-scale RGB-D object dataset show that our kernels significantly outperform the state-of-the-art results by using a single 3D shape feature type extracted from individual depth images.