Min-cost Flow Algorithm Research Articles

Tracking multiple targets based on min-cost network flows with detection in RGB-D data

Visual multi-target tracking technology is a challenging problem in computer vision. This study proposes a novel approach for multi-target tracking based on min-cost network flows in RGB-D data with tracking-by-detection scheme. Firstly, the moving objects are detected by fusing RGB information and depth information. Then, we formulate the multi-target tracking problem as a maximum a posteriori (MAP) estimation problem with specific constraints, and the problem is converted into a cost-flow network. Finally, using a min-cost flow algorithm, we can obtain the tracking results. Extensive experimental results show that the proposed algorithm greatly improves the robustness and accuracy and outperforms the state-of-the-art significantly.

Earth mover's distance based similarity search at scale

Earth Mover's Distance (EMD), as a similarity measure, has received a lot of attention in the fields of multimedia and probabilistic databases, computer vision, image retrieval, machine learning, etc. EMD on multidimensional histograms provides better distinguishability between the objects approximated by the histograms (e.g., images), compared to classic measures like Euclidean distance. Despite its usefulness, EMD has a high computational cost; therefore, a number of effective filtering methods have been proposed, to reduce the pairs of histograms for which the exact EMD has to be computed, during similarity search. Still, EMD calculations in the refinement step remain the bottleneck of the whole similarity search process. In this paper, we focus on optimizing the refinement phase of EMD-based similarity search by (i) adapting an efficient min-cost flow algorithm (SIA) for EMD computation, (ii) proposing a dynamic distance bound, which can be used to terminate an EMD refinement early, and (iii) proposing a dynamic refinement order for the candidates which, paired with a concurrent EMD refinement strategy, reduces the amount of needless computations. Our proposed techniques are orthogonal to and can be easily integrated with the state-of-the-art filtering techniques, reducing the cost of EMD-based similarity queries by orders of magnitude.

MSV-Driven Floorplanning

Power consumption has become a crucial problem in modern circuit design. Multiple supply voltage (MSV) design is introduced to provide higher flexibility in controlling the power and performance tradeoff. One important requirement of MSV design is that timing constraints of the circuit must be satisfied after voltage assignment of the cells. In this article, we develop two algorithms to solve the voltage assignment problem under timing constraints, namely, min-cost flow (MCF) and value-oriented branch-and-bound (VOBB). In the MCF algorithm, the voltage assignment problem is formulated as a convex cost dual network flow problem, and can be solved optimally in polynomial time under certain conditions by calling a MCF solver. The VOBB algorithm, which is a VOBB-based searching method, solves the voltage assignment problem optimally in general cases by employing the MCF algorithm and a linear programming solver as subroutines. At last, we propose a MSV-driven floorplanning framework that optimizes power consumption and physical layout of a circuit simultaneously during the floorplanning stage, by embedding the MCF algorithm into a simulated annealing-based floorplanner and applying the VOBB algorithm as a postprocessing step. We compared our approach with the latest works on this problem, and the experimental results show that, using our approach, significant improvement on power saving can be achieved in much less running time, which confirms the effectiveness and efficiency of our method.