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Visual tracking decomposition

Visual tracking decomposition

引用

conference on computer vision and pattern recognition (CVPR)

作者： Junseok Kwon Kyoung Mu Lee Department of EECS ASRI Seoul National University Seoul South Korea

We propose a novel tracking algorithm that can work robustly in a challenging scenario such that several kinds of appearance and motion changes of an object occur at the same time. Our algorithm is based on a visual tracking decomposition scheme for the efficient design of observation and motion models as well as trackers. In our scheme, the observation model is decomposed into multiple basic observation models that are constructed by sparse principal component analysis (SPCA) of a set of feature templates. Each basic observation model covers a specific appearance of the object. The motion model is also represented by the combination of multiple basic motion models, each of which covers a different type of motion. Then the multiple basic trackers are designed by associating the basic observation models and the basic motion models, so that each specific tracker takes charge of a certain change in the object. All basic trackers are then integrated into one compound tracker through an interactive Markov Chain Monte Carlo (IMCMC) framework in which the basic trackers communicate with one another interactively while run in parallel. By exchanging information with others, each tracker further improves its performance, which results in increasing the whole performance of tracking. Experimental results show that our method tracks the object accurately and reliably in realistic videos where the appearance and motion are drastically changing over time.

关键词： Robustness Videos Target tracking Lighting Algorithm design and analysis Principal component analysis Monte Carlo methods computer vision Photometry Fuses

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Anatomical parts-based regression using non-negative matrix factorization

Anatomical parts-based regression using non-negative matrix ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Swapna Joshi S. Karthikeyan B. S. Manjunath Scott Grafton Kent A. Kiehl Department of Electrical and Computer Engineering University of California Santa Barbara USA Department of Psychology University of California Santa Barbara USA Department Psychology University of New Mexico USA

Non-negative matrix factorization (NMF) is an excellent tool for unsupervised parts-based learning, but proves to be ineffective when parts of a whole follow a specific pattern. Analyzing such local changes is particularly important when studying anatomical transformations. We propose a supervised method that incorporates a regression constraint into the NMF framework and learns maximally changing parts in the basis images, called Regression based NMF (RNMF). The algorithm is made robust against outliers by learning the distribution of the input manifold space, where the data resides. One of our main goals is to achieve good region localization. By incorporating a gradient smoothing and independence constraint into the factorized bases, contiguous local regions are captured. We apply our technique to a synthetic dataset and structural MRI brain images of subjects with varying ages. RNMF finds the localized regions which are expected to be highly changing over age to be manifested in its significant basis and it also achieves the best performance compared to other statistical regression and dimensionality reduction techniques.

关键词： Magnetic resonance imaging Psychology Anatomy Biomedical imaging Robustness Smoothing methods Diseases Data mining Principal component analysis Independent component analysis

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Large-scale image categorization with explicit data embedding

Large-scale image categorization with explicit data embeddin...

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conference on computer vision and pattern recognition (CVPR)

作者： Florent Perronnin Jorge Sánchez Yan Liu Xerox Research Centre Europe (XRCE) France Xerox Meylan France

Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel machines are difficult to scale to large training sets, it has been proposed to perform an explicit mapping of the data and to learn directly linear classifiers in the new space. In this paper, we consider the problem of learning image categorizers on large image sets (e.g. > 100k images) using bag-of-visual-words (BOV) image representations and Support Vector Machine classifiers. We experiment with three approaches to BOV embedding: 1) kernel PCA (kPCA), 2) a modified kPCA we propose for additive kernels and 3) random projections for shift-invariant kernels. We report experiments on 3 datasets: Cal-tech101, VOC07 and ImageNet. An important conclusion is that simply square-rooting BOV vectors - which corresponds to an exact mapping for the Bhattacharyya kernel - already leads to large improvements, often quite close to the best results obtained with additive kernels. Another conclusion is that, although it is possible to go beyond additive kernels, the embedding comes at a much higher cost.

关键词： Large-scale systems Kernel Support vector machines Support vector machine classification Costs Europe Machine learning Image representation Principal component analysis computer vision

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Outlier removal using duality

Outlier removal using duality

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conference on computer vision and pattern recognition (CVPR)

作者： Carl Olsson Anders Eriksson Richard Hartley Centre for Mathematical Sciences Lund University Sweden School of Computer Science University of Adelaide Australia Australian National University National ICT Australia Limited Australia

In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method for this task is RANSAC. However, as RANSAC only works on a subset of the images, mismatches in longer point tracks may go undetected. To deal with this problem we would like to have, as a post processing step to RANSAC, a method that works on the entire (or a larger) part of the sequence. In this paper we consider two algorithms for doing this. The first one is related to a method by Sim & Hartley where a quasiconvex problem is solved repeatedly and the error residuals with the largest error is removed. Instead of solving a quasiconvex problem in each step we show that it is enough to solve a single LP or SOCP which yields a significant speedup. Using duality we show that the same theoretical result holds for our method. The second algorithm is a faster version of the first, and it is related to the popular method of L 1 -optimization. While it is faster and works very well in practice, there is no theoretical guarantee of success. We show that these two methods are related through duality, and evaluate the methods on a number of data sets with promising results.

关键词： Image reconstruction Geometry Cameras computer science Large-scale systems Image sequences Statistical distributions Motion estimation Layout computer errors

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Online multiple instance learning with no regret

Online multiple instance learning with no regret

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conference on computer vision and pattern recognition (CVPR)

作者： Mu Li James T. Kwok Bao-Liang Lu Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University Shanghai China

Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been used in a wide range of applications including image classification, object detection and object tracking. Typically, MI algorithms are trained in a batch setting in which the whole training set has to be available before training starts. However, in applications such as tracking, the classifier needs to be trained continuously as new frames arrive. Motivated by the empirical success of a batch MI algorithm called MILES, we propose in this paper an online MI learning algorithm that has an efficient online update procedure and also performs joint feature selection and classification as MILES. Besides, while existing online MI algorithms lack theoretical properties, we prove that the proposed online algorithm has a (cumulative) regret of O(√T), where T is the number of iterations. In other words, the average regret goes to zero asymptotically and it thus achieves the same performance as the best solution in hindsight. Experiments on a number of MI classification and object tracking data sets demonstrate encouraging results.

关键词： Layout Image reconstruction Semiconductor device modeling Image segmentation Stereo vision Surface reconstruction Surface texture Surface fitting Stereo image processing Rendering (computer graphics)

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Improving state-of-the-art OCR through high-precision document-specific modeling

Improving state-of-the-art OCR through high-precision docume...

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conference on computer vision and pattern recognition (CVPR)

作者： Andrew Kae Gary Huang Carl Doersch Erik Learned-Miller Department of Computer Science University of Massachusetts Amherst USA Department of Computer Science Carnegie Mellon University USA

Optical character recognition (OCR) remains a difficult problem for noisy documents or documents not scanned at high resolution. Many current approaches rely on stored font models that are vulnerable to cases in which the document is noisy or is written in a font dissimilar to the stored fonts. We address these problems by learning character models directly from the document itself, rather than using pre-stored font models. This method has had some success in the past, but we are able to achieve substantial improvement in error reduction through a novel method for creating nearly error-free document-specific training data and building character appearance models from this data. In particular, we first use the state-of-the-art OCR system Tesseract to produce an initial translation. Then, our method identifies a subset of words that we have high confidence have been recognized correctly and uses this subset to bootstrap document-specific character models. We present theoretical justification that a word in the selected subset is very unlikely to be incorrectly recognized, and empirical results on a data set of difficult historical newspaper scans demonstrating that we make only two errors in 56 documents. We then relax the theoretical constraint in order to create a larger training set, and using document-specific character models generated from this data, we are able to reduce the error over properly segmented characters by 34.1% overall from the initial Tesseract translation.

关键词： Optical character recognition software Training data Error correction Optical noise Character recognition computer science Constraint theory Character generation Error analysis

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Linked edges as stable region boundaries

Linked edges as stable region boundaries

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conference on computer vision and pattern recognition (CVPR)

作者： Michael Donoser Hayko Riemenschneider Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria

Many of the recently popular shape based category recognition methods require stable, connected and labeled edges as input. This paper introduces a novel method to find the most stable region boundaries in grayscale images for this purpose. In contrast to common edge detection algorithms as Canny, which only analyze local discontinuities in image brightness, our method integrates mid-level information by analyzing regions that support the local gradient magnitudes. We use a component tree where every node contains a single connected region obtained from thresholding the gradient magnitude image. Edges in the tree are defined by an inclusion relationship between nested regions in different levels of the tree. Region boundaries which are similar in shape (i. e. have a low chamfer distance) across several levels of the tree are included in the final result. Since the component tree can be calculated in quasi-linear time and chamfer matching between nodes in the component tree is reduced to analysis of the distance transformation, results are obtained in an efficient manner. The proposed detection algorithm labels all identified edges during calculation, thus avoiding the cumbersome post-processing of connecting and labeling edge responses. We evaluate our method on two reference data sets and demonstrate improved performance for shape prototype based localization of objects in images.

关键词： Image edge detection Shape Detection algorithms Image analysis Information analysis Algorithm design and analysis Gray-scale Brightness Joining processes Labeling

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Online visual vocabulary pruning using pairwise constraints

Online visual vocabulary pruning using pairwise constraints

引用

conference on computer vision and pattern recognition (CVPR)

作者： Pavan K. Mallapragada Rong Jin Anil K. Jain Department Computer Science and Engineering Michigan State University East Lansing MI USA Department Brain and Cognitive Engineering Korea University Seoul South Korea

Given a pair of images represented using bag-of-visual-words and a label corresponding to whether the images are “related”(must-link constraint) or “unrelated” (cannot-link constraint), we address the problem of selecting a subset of visual words that are salient in explaining the relation between the image pair. In particular, a subset of features is selected such that the distance computed using these features satisfies the given pairwise constraints. An efficient online feature selection algorithm is presented based on the dual-gradient descent approach. Side information in the form of pair-wise constraints is incorporated into the feature selection stage, providing the user with flexibility to use an unsupervised or semi-supervised algorithm at a later stage. Correlated subsets of visual words, usually resulting from hierarchical quantization process (called groups), are exploited to select a significantly smaller vocabulary. A group-LASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experiments on PASCAL VOC 2007 dataset using 5000 visual keywords, resulted in around 80% reduction in the number of keywords, with little or no loss in performance.

关键词： Vocabulary Clustering algorithms computer science Quantization Performance loss Prototypes Large-scale systems Image retrieval Scalability Partitioning algorithms

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Rapid and accurate developmental stage recognition of C. elegans from high-throughput image data

Rapid and accurate developmental stage recognition of C. ele...

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conference on computer vision and pattern recognition (CVPR)

作者： Amelia G. White Patricia G. Cipriani Huey-Ling Kao Brandon Lees Davi Geiger Eduardo Sontag Kristin C. Gunsalus Fabio Piano Center for Genomics and Systems Biology and Department of Biology New York University New York NY USA BioMaPS Institute Rutgers University Piscataway NJ USA Department of Computer Science New York University New York NY USA Department of Mathematics Rutgers University Piscataway NJ USA

We present a hierarchical principle for object recognition and its application to automatically classify developmental stages of C. elegans animals from a population of mixed stages. The object recognition machine consists of four hierarchical layers, each composed of units upon which evaluation functions output a label score, followed by a grouping mechanism that resolves ambiguities in the score by imposing local consistency constraints. Each layer then outputs groups of units, from which the units of the next layer are derived. Using this hierarchical principle, the machine builds up successively more sophisticated representations of the objects to be classified. The algorithm segments large and small objects, decomposes objects into parts, extracts features from these parts, and classifies them by SVM. We are using this system to analyze phenotypic data from C. elegans high-throughput genetic screens, and our system overcomes a previous bottleneck in image analysis by achieving near real-time scoring of image data. The system is in current use in a functioning C. elegans laboratory and has processed over two hundred thousand images for lab users.

关键词： Image recognition Object recognition Image analysis Animals Image segmentation Data mining Feature extraction Support vector machines Support vector machine classification Data analysis

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Transductive segmentation of live video with non-stationary background

Transductive segmentation of live video with non-stationary ...

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conference on computer vision and pattern recognition (CVPR)

作者： Fan Zhong Xueying Qin Qunsheng Peng State Key Laboratory of CAD&CG University of Zhejiang China Department of Computer Science Shandong University China

Online foreground extraction is very difficult due to the complexity of real scenes. Almost all the previous methods assume that the background is stationary, which not only incur unreliable result due to background activities like dynamic shadow, moving background objects etc., but also makes them hard to be extended to the case of non-stationary background. In this paper we assume that the background is continuous instead of stationary, and present a transductive video segmentation method that can handle dynamic scenes captured by a hand-held moving camera. The segmentation is propagated based on local color models and temporal prior, as well as a dynamic global color model (DGKDE) in the case of occlusion. A novel local color modeling method, FLKDE, is proposed to model both local color distribution and temporal prior at each pixel. FLKDE can be learned additively to reach real-time speed. Finally, a very fast geodesic-based method is adopted to solve for the segmentation. Experiments show that our method can generate good quality segmentation for wide variety of scenes, and can reach 15~25 fps for 640 × 480 size of input image sequences.

关键词： Layout Image segmentation Cameras Image sequences computer science Augmented reality Real time systems Network synthesis Differential equations Sun

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