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A new multiple classifier system (MCS) is proposed based on CTSP (classification based on Testing Sample Pairs), which is a kind of applicable and efficient classification method. However, the original output form of the CTSP is only crisp class labels. To make use of the information provided by the classifier, in this paper, the output of CTSP is modeled using the membership function. Then, the fuzzy-cautious...
Dempster-Shafer theory (DST) is an important theory for information fusion. However, in DST how to determinate the basic belief assignment (BBA) is still an open issue. The interval number based BBA determination method is simple and effective, where the features of different classes' samples are modeled using the interval numbers, i.e., an interval number model is constructed for each focal element...
Visual Background Extractor (ViBe) is a video moving object detection method with simple implementation and fast speed. ViBe uses a detection threshold (neighborhood size) to judge whether a pixel belongs to the background or the foreground. However, in some complicated scenes, the belongingness of the pixels is ambiguous. One cannot well perform the object detection using the ViBe with a single threshold,...
The ranking fusion (or aggregation), which is an important branch in multiple attribute decision making, combines multiple rankings to a single one for decision making. Many traditional ranking fusion methods are implemented through heuristic ways to reduce the computational cost. They all have their own pros and cons. In this paper, a new hierarchical ranking aggregation method is proposed. All the...
Modern sensors can acquire attributes in addition to the kinematic measurements of targets. Track association performance could be improved by using these attributes jointly. Therefore, a fast track association using multiple attributes is proposed. It converts the track-to-track association to the sub-track-to-systematic track association and reduces the times of association judgments. Sub-tracks...
The ranking fusion (or aggregation), which is an important type of multiple attribute decision making (MADM), combines multiple rankings to a single one for decision making. In traditional ranking fusion, one branch of approaches accomplish the ranking fusion in one run, i.e., the ranking positions of the items concerned are determined simultaneously. Another branch of approaches are based on the...
A novel multiple classifiers fusion approach based on SFLS (Shortest Feature Line Segment) is proposed in this paper. SFLS is a kind of simple yet effective classification method depending on the shortest feature line. The original form of SFLS's output is just class label. To use SFLS as the member classifier in the multiple classifier system, the form of SFLS's output is modeled using the membership...
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