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19
Maximum weight cliques with mutex constraints for video object segmentation
, 2012
"... In this paper, we address the problem of video object segmentation, which is to automatically identify the primary object and segment the object out in every frame. We propose a novel formulation of selecting object region candidates simultaneously in all frames as finding a maximum weight clique in ..."
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In this paper, we address the problem of video object segmentation, which is to automatically identify the primary object and segment the object out in every frame. We propose a novel formulation of selecting object region candidates simultaneously in all frames as finding a maximum weight clique in a weighted region graph. The selected regions are expected to have high objectness score (unary potential) as well as share similar appearance (binary potential). Since both unary and binary potentials are unreliable, we introduce two types of mutex (mutual exclusion) constraints on regions in the same clique: intraframe and interframe constraints. Both types of constraints are expressed in a single quadratic form. We propose a novel algorithm to compute the maximal weight cliques that satisfy the constraints. We apply our method to challenging benchmark videos and obtain very competitive results that outperform stateoftheart methods.
Semisupervised learning and optimization for hypergraph matching
 In IEEE International Conference on Computer Vision
, 2011
"... Graph and hypergraph matching are important problems in computer vision. They are successfully used in many applications requiring 2D or 3D feature matching, such as 3D reconstruction and object recognition. While graph matching is limited to using pairwise relationships, hypergraph matching permits ..."
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Cited by 8 (1 self)
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Graph and hypergraph matching are important problems in computer vision. They are successfully used in many applications requiring 2D or 3D feature matching, such as 3D reconstruction and object recognition. While graph matching is limited to using pairwise relationships, hypergraph matching permits the use of relationships between sets of features of any order. Consequently, it carries the promise to make matching more robust to changes in scale, deformations and outliers. In this paper we make two contributions. First, we present a first semisupervised algorithm for learning the parameters that control the hypergraph matching model and demonstrate experimentally that it significantly improves the performance of current stateoftheart methods. Second, we propose a novel efficient hypergraph matching algorithm, which outperforms the stateoftheart, and, when used in combination with other higherorder matching algorithms, it consistently improves their performance. 1.
Efficient Hypergraph Clustering
"... Data clustering is an essential problem in data mining, machine learning and computer vision. In this paper we present a novel method for the hypergraph clustering problem, in which second or higher order affinities between sets of data points are considered. Our algorithm has important theoretical ..."
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Cited by 4 (1 self)
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Data clustering is an essential problem in data mining, machine learning and computer vision. In this paper we present a novel method for the hypergraph clustering problem, in which second or higher order affinities between sets of data points are considered. Our algorithm has important theoretical properties, such as convergence and satisfaction of first order necessary optimality conditions. It is based on an efficient iterative procedure, which by updating the cluster membership of all points in parallel, is able to achieve state of the art results in very few steps. We outperform current hypergraph clustering methods especially in terms of computational speed, but also in terms of accuracy. Moreover, we show that our method could be successfully applied both to higherorder assignment problems and to image segmentation. 1
D.: Discretecontinuous gradient orientation estimation for faster image segmentation
"... The stateoftheart in image segmentation builds hierarchical segmentation structures based on analyzing local feature cues in spectral settings. Due to their impressive performance, such segmentation approaches have become building blocks in many computer vision applications. Nevertheless, the mai ..."
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The stateoftheart in image segmentation builds hierarchical segmentation structures based on analyzing local feature cues in spectral settings. Due to their impressive performance, such segmentation approaches have become building blocks in many computer vision applications. Nevertheless, the main bottlenecks are still the computationally demanding processes of local feature processing and spectral analysis. In this paper, we demonstrate that based on a discretecontinuous optimization of oriented gradient signals, we are able to provide segmentation performance competitive to stateoftheart on BSDS 500 (even without any spectral analysis) while reducing computation time by a factor of 30 and memory demands by a factor of 10. 1.
Clustering Aggregation as MaximumWeight Independent Set
"... We formulate clustering aggregation as a special instance of MaximumWeight Independent Set (MWIS) problem. For a given dataset, an attributed graph is constructed from the union of the input clusterings generated by different underlying clustering algorithms with different parameters. The vertices, ..."
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We formulate clustering aggregation as a special instance of MaximumWeight Independent Set (MWIS) problem. For a given dataset, an attributed graph is constructed from the union of the input clusterings generated by different underlying clustering algorithms with different parameters. The vertices, which represent the distinct clusters, are weighted based on an internal index measuring both cohesion and separation. The edges connect the vertices whose corresponding clusters overlap. Intuitively, an optimal aggregated clustering can be obtained by selecting an optimal subset of nonoverlapping clusters partitioning the dataset together. We formalize this intuition as the MWIS problem on the attributed graph, i.e., finding the heaviest subset of mutually nonadjacent vertices. This MWIS problem exhibits a special structure. Since the clusters of each input clustering form a partition of the dataset, the vertices corresponding to each clustering form a maximal independent set (MIS) in the attributed graph. We propose a variant of simulated annealing method that takes advantage of this special structure. Our algorithm starts from each MIS, which is close to a distinct local optimum of the MWIS problem, and utilizes a local search heuristic to explore its neighborhood in order to find the MWIS. Extensive experiments on many challenging datasets show that: 1. our approach to clustering aggregation automatically decides the optimal number of clusters; 2. it does not require any parameter tuning for the underlying clustering algorithms; 3. it can combine the advantages of different underlying clustering algorithms to achieve superior performance; 4. it is robust against moderate or even bad input clusterings. 1
1PTEC: A System for Predictive Thermal and Energy Control in Data Centers
"... Abstract—Current data centers often adopt conservative and static settings for cooling and air circulation systems, leading to excessive energy consumption. This paper presents the design and evaluation of PTEC – a system for predictive thermal and energy control in data centers. PTEC leverages the ..."
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Abstract—Current data centers often adopt conservative and static settings for cooling and air circulation systems, leading to excessive energy consumption. This paper presents the design and evaluation of PTEC – a system for predictive thermal and energy control in data centers. PTEC leverages the server builtin sensors and monitoring utilities, as well as a wireless sensor network, to monitor both the cyber and physical status of a data center. By predicting the temperature evolution of a data center in real time, PTEC finds the temperature setpoints, the cold air supply rates, and the speeds of server internal fans to minimize the expected total energy consumption of cooling and circulation systems. Moreover, PTEC enforces the upper bounds on server inlet temperatures and their temporal variations to prevent server overheating and reduce server hardware failure rate. We evaluated PTEC on a hardware testbed consisting of 15 servers and a total of 23 temperature and power sensors, as well as through Computational Fluid Dynamics (CFD) simulations based on real data traces collected from a data center with 229 servers. The experimental results show that PTEC can reduce the cooling and circulation energy consumption by more than 30%, compared with baseline thermal control strategies. I.
Hierarchical radio resource optimization for heterogeneous networks with enhanced intercell interference coordination (eICIC
 IEEE Trans. Signal Process
, 2014
"... Abstract—Interference is a major performance bottleneck in Heterogeneous Network (HetNet) due to its multitier topological structure. We propose almost blank resource block (ABRB) for interference control in HetNet. When an ABRB is scheduled in a macro BS, a resource block (RB) with blank payload i ..."
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Abstract—Interference is a major performance bottleneck in Heterogeneous Network (HetNet) due to its multitier topological structure. We propose almost blank resource block (ABRB) for interference control in HetNet. When an ABRB is scheduled in a macro BS, a resource block (RB) with blank payload is transmitted and this eliminates the interference from this macro BS to the pico BSs. We study a two timescale hierarchical radio resource management (RRM) scheme for HetNet with dynamic ABRB control. The long term controls, such as dynamic ABRB, are adaptive to the large scale fading at a RRM server for coTier and crossTier interference control. The short term control (user scheduling) is adaptive to the local channel state information within each BS to exploit the multiuser diversity. The two timescale optimization problem is challenging due to the exponentially large solution space. We exploit the sparsity in the interference graph of the HetNet topology and derive structural properties for the optimal ABRB control. Based on that, we propose a two timescale alternative optimization solution for the user scheduling and ABRB control. The solution has low complexity and is asymptotically optimal at high SNR. Simulations show that the proposed solution has significant gain over various baselines.
Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cells
"... Motivation: To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, socalled trackingbyassignment methods are flexible approaches. However, as every twostage approa ..."
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Motivation: To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, socalled trackingbyassignment methods are flexible approaches. However, as every twostage approach, they suffer from irrevocable errors propagated from the first stage to the second stage, here: from segmentation to tracking. It is therefore desirable to model segmentation and tracking in a joint holistic assignment framework allowing the two stages to maximally benefit from each other. Results: We propose a probabilistic graphical model which both automatically selects the best segments from a timeseries of oversegmented images/volumes and links them across time. This is realized by introducing intraframe and interframe constraints between conflicting segmentation and tracking hypotheses while at the same time allowing for cell division. We show the efficiency of our algorithm on a challenging 3D+t cell tracking dataset from Drosophila embryogenesis as well as on a 2D+t dataset of proliferating cells in a dense population with frequent overlaps. On the latter, we achieve results significantly better than stateoftheart tracking methods. Availability: Source code and the 3D+t Drosophila dataset along with our manual annotations are freely available on
HIGHER ORDER MARKOV RANDOM FIELDS FOR INDEPENDENT SETS
"... It is wellknown that if one samples from the independent sets of a large regular graph of large girth using a pairwise Markov random eld (i.e. hardcore model) in the uniqueness regime, each excluded node has a binomially distributed number of included neighbors in the limit. In this paper, motivate ..."
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It is wellknown that if one samples from the independent sets of a large regular graph of large girth using a pairwise Markov random eld (i.e. hardcore model) in the uniqueness regime, each excluded node has a binomially distributed number of included neighbors in the limit. In this paper, motivated by applications to the design of communication networks, we pose the question of how to sample from the independent sets of such a graph so that the number of included neighbors of each excluded node has a dierent distribution of our choosing. We observe that higher order Markov random elds are wellsuited to this task, and investigate the properties of these models. For the family of socalled reverse ultra logconcave distributions, which includes the truncated Poisson and geometric, we give necessary and sucient conditions for the natural higher order Markov random eld which induces the desired distribution to be in the uniqueness regime, in terms of the set of solutions to a certain system of equations. We also show that these Markov random elds undergo a phase transition, and give explicit bounds on the associated critical activity, which we prove to exhibit a certain robustness. For distributions which are small perturbations around the binomial distribution realized by the hardcore model at critical activity, we give a description of the corresponding uniqueness regime in terms of a simple polyhedral cone. Our analysis reveals an interesting nonmonotonicity with regards to biasing towards excluded nodes with no included neighbors. We conclude with a broader discussion of the potential use of higher order Markov random elds for analyzing independent sets in graphs. 1. Introduction. Recently