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Search Results: 1 - 10 of 8687 matches for " Vladimir Pavlovic "
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Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation
Saehoon Yi,Vladimir Pavlovic
Computer Science , 2015,
Abstract: Video segmentation is a stepping stone to understanding video context. Video segmentation enables one to represent a video by decomposing it into coherent regions which comprise whole or parts of objects. However, the challenge originates from the fact that most of the video segmentation algorithms are based on unsupervised learning due to expensive cost of pixelwise video annotation and intra-class variability within similar unconstrained video classes. We propose a Markov Random Field model for unconstrained video segmentation that relies on tight integration of multiple cues: vertices are defined from contour based superpixels, unary potentials from temporal smooth label likelihood and pairwise potentials from global structure of a video. Multi-cue structure is a breakthrough to extracting coherent object regions for unconstrained videos in absence of supervision. Our experiments on VSB100 dataset show that the proposed model significantly outperforms competing state-of-the-art algorithms. Qualitative analysis illustrates that video segmentation result of the proposed model is consistent with human perception of objects.
Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks
Jongpil Kim,Vladimir Pavlovic
Computer Science , 2015,
Abstract: In this paper, we propose a novel method to find characteristic landmarks on ancient Roman imperial coins using deep convolutional neural network models (CNNs). We formulate an optimization problem to discover class-specific regions while guaranteeing specific controlled loss of accuracy. Analysis on visualization of the discovered region confirms that not only can the proposed method successfully find a set of characteristic regions per class, but also the discovered region is consistent with human expert annotations. We also propose a new framework to recognize the Roman coins which exploits hierarchical structure of the ancient Roman coins using the state-of-the-art classification power of the CNNs adopted to a new task of coin classification. Experimental results show that the proposed framework is able to effectively recognize the ancient Roman coins. For this research, we have collected a new Roman coin dataset where all coins are annotated and consist of observe (head) and reverse (tail) images.
Efficient motif finding algorithms for large-alphabet inputs
Kuksa Pavel P,Pavlovic Vladimir
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-s8-s1
Abstract: Background We consider the problem of identifying motifs, recurring or conserved patterns, in the biological sequence data sets. To solve this task, we present a new deterministic algorithm for finding patterns that are embedded as exact or inexact instances in all or most of the input strings. Results The proposed algorithm (1) improves search efficiency compared to existing algorithms, and (2) scales well with the size of alphabet. On a synthetic planted DNA motif finding problem our algorithm is over 10× more efficient than MITRA, PMSPrune, and RISOTTO for long motifs. Improvements are orders of magnitude higher in the same setting with large alphabets. On benchmark TF-binding site problems (FNP, CRP, LexA) we observed reduction in running time of over 12×, with high detection accuracy. The algorithm was also successful in rapidly identifying protein motifs in Lipocalin, Zinc metallopeptidase, and supersecondary structure motifs for Cadherin and Immunoglobin families. Conclusions Our algorithm reduces computational complexity of the current motif finding algorithms and demonstrate strong running time improvements over existing exact algorithms, especially in important and difficult cases of large-alphabet sequences.
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
Changkyu Song,Sejong Yoon,Vladimir Pavlovic
Mathematics , 2015,
Abstract: We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including a computer vision application of distributed structure from motion.
Learning Hypergraph Labeling for Feature Matching
Toufiq Parag,Vladimir Pavlovic,Ahmed Elgammal
Computer Science , 2011,
Abstract: This study poses the feature correspondence problem as a hypergraph node labeling problem. Candidate feature matches and their subsets (usually of size larger than two) are considered to be the nodes and hyperedges of a hypergraph. A hypergraph labeling algorithm, which models the subset-wise interaction by an undirected graphical model, is applied to label the nodes (feature correspondences) as correct or incorrect. We describe a method to learn the cost function of this labeling algorithm from labeled examples using a graphical model training algorithm. The proposed feature matching algorithm is different from the most of the existing learning point matching methods in terms of the form of the objective function, the cost function to be learned and the optimization method applied to minimize it. The results on standard datasets demonstrate how learning over a hypergraph improves the matching performance over existing algorithms, notably one that also uses higher order information without learning.
Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images
Ognjen Rudovic,Maja Pantic,Vladimir Pavlovic
Computer Science , 2013,
Abstract: We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ranks, that impose temporal ordinal constraints on the static ranks (i.e., intensity scores). Our experimental results show that the proposed approach outperforms state-of-the art methods for sequence classification with ordinal data and other ordinal regression models. The approach performs significantly better than other models in terms of Intra-Class Correlation measure, which is the most accepted evaluation measure in the tasks of facial behaviour intensity estimation.
Gaussian Process for Noisy Inputs with Ordering Constraints
Cuong Tran,Vladimir Pavlovic,Robert Kopp
Statistics , 2015,
Abstract: We study the Gaussian Process regression model in the context of training data with noise in both input and output. The presence of two sources of noise makes the task of learning accurate predictive models extremely challenging. However, in some instances additional constraints may be available that can reduce the uncertainty in the resulting predictive models. In particular, we consider the case of monotonically ordered latent input, which occurs in many application domains that deal with temporal data. We present a novel inference and learning approach based on non-parametric Gaussian variational approximation to learn the GP model while taking into account the new constraints. The resulting strategy allows one to gain access to posterior estimates of both the input and the output and results in improved predictive performance. We compare our proposed models to state-of-the-art Noisy Input Gaussian Process (NIGP) and other competing approaches on synthetic and real sea-level rise data. Experimental results suggest that the proposed approach consistently outperforms selected methods while, at the same time, reducing the computational costs of learning and inference.
BF3 etherate-induced formation of C3-11-alkenyl 2,3-unsaturated glucosides
Journal of the Serbian Chemical Society , 2001,
Abstract: BF3 etherate-induced formation of C3-11 -alkenyl 2,3-unsaturated glucosides was used as the key step in their synthesis from glucose and C3 -C11 -alkenols.
The Ferrier rearrangement as the key step in the synthesis of C7 C16-alkyl 2,3-dideoxy glucosides from glucose and C7 C16-alkanols
Journal of the Serbian Chemical Society , 2001,
Abstract: The Ferrier rearrangment was used as the key step in the synthesis of C7 C16-alkyl 2,3-dideoxy glucosides from glucose and C7 C16-alkanols.
SnCl4 induced formation of C7 C16-alkyl D-glucopyranosides
Journal of the Serbian Chemical Society , 2001,
Abstract: The SnCl4 catalyzed glycosylation reaction of b-peracetylated sugar derivative (glucose) with fatty alkanols is used in the synthesis of C7 C16 -alkyl glucopyranosides.
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