On the reinforcement of uninorms and absorbing norms

DUKE_HCERES2020Aggregation operators Reinforcement ... We propose a n-ary extension of absorbing norms, defined with the help of generative functions, and its relationship with additive generating functions of uninorms. In this paper, we also present new aggregation operators, namely the k-uninorms and k-absorbing norms. These operators are a generalization of usual uninorms and absorbing norms for which a set combination of inputs is introduced. Their main ability is to provide reinforcement for contradictory inputs, as nullnorms and as opposed to uninorms. On the other hand it still provides full reinforcement for agreeing inputs, as uninorms and as opposed to nullnorms. Numerous examples are given in order to illustrate the behavior of the proposed operators

Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation

International audienceBackground subtraction (BS) is one of the key steps for detecting moving objects in video surveillance applications. In the last few years, many BS methods have been developed to handle the different challenges met in video surveillance but the role and the relevance of the visual features used has been less investigated. In this paper, we present an Online Weighted Ensemble of One-Class SVMs (Support Vector Machines) able to select suitable features for each pixel to distinguish the foreground objects from the background. In addition, our proposal uses a mechanism to update the relative importance of each feature over time. Moreover, a heuristic approach is used to reduce the complexity of the background model maintenance while maintaining the robustness of the background model. Results on two datasets show the pertinence of the approach

A class-selective rejection scheme based on blockwise similarity of typicality degrees

Overlapping classes and outliers can significantly decrease a classifier performance. We adress here the problem of giving a classifier the ability to reject some patterns either for ambiguity or for distance in order to improve its performance. Given a set of typicality degrees for a pattern to be classified, we use an operator based on triangular norms and a discrete Sugeno integral to quantify their blockwise similarities. We propose a new class-selective rejection scheme which uses this operator outputs. We present the resulting algorithm which allows to assign a pattern to zero, one or several classes, and show its efficiency on real data sets. 1