In recent years, the addition-min fuzzy relation inequalities have been adopted to describe the flow constraint in a P2P network system. Each solution of the inequalities represents a feasible flow control scheme. Mot...
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In recent years, the addition-min fuzzy relation inequalities have been adopted to describe the flow constraint in a P2P network system. Each solution of the inequalities represents a feasible flow control scheme. Motivated by some different managerial objectives, several optimization problems subject to the addition-min inequalities have been recently studied. For example, considering the total efficiency for decreasing the network congestion, the linear objectivefunction was adopted. While considering the fairness among the terminals, the min-max objective function was employed. In this article, combining these two objectives, we establish a corresponding bilevel fuzzy relation programming subject to the addition-min inequalities. For solving the bilevel programming, we first investigate some properties of the first-level programming, using the concept of fixed index set. Based on the properties of the first-level programming, our studied bilevel programming could be equivalently converted into a single-level programming and then solved by the existing linear programming approach. Our resolution approach is carried out by the fixed-index-set based algorithm and illustrated by some numerical examples.
In this paper we introduce an enhanced graph partition model, more robust, used for video sequences temporal segmentation. In the first part we examine the formal framework of the shot boundary detection techniques im...
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ISBN:
(纸本)9781457702013
In this paper we introduce an enhanced graph partition model, more robust, used for video sequences temporal segmentation. In the first part we examine the formal framework of the shot boundary detection techniques implemented in the recent years, emphasizing the weakness and strength for each method. In the second phase we present our novel algorithm that applies a scale space median filtering on a min-max objective function that optimize the association within each graph cut in order to remove noise caused by camera movement or large object displacement. The algorithm evaluation is done on a subset of movies from the TRECVID 2001 and 2002 video database which demonstrate the improved performance of our method regardless to the noise gender, with precision and recall rates of average 90% and 86 %, respectively. In the end we conclude the paper and present some further implementation.
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