On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
The ordered weighted averaging operators
An analytic approach for obtaining maximal entropy OWA operator weights
Fuzzy Sets and Systems
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Information-theoretic semantic multimedia indexing
Proceedings of the 6th ACM international conference on Image and video retrieval
Portfolio theory of multimedia fusion
Proceedings of the international conference on Multimedia
A Learning to Rank framework applied to text-image retrieval
Multimedia Tools and Applications
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
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We present a novel multi-modal evidence fusion method for highlevel feature (HLF) detection in videos. The uni-modal features, such as color histogram, transcript texts, etc, tend to capture different aspects of HLFs and hence share complementariness and redundancy in modeling the contents of such HLFs. We argue that such inter-relation are key to effective multi-modal fusion. Here, we formulate the fusion as a multi-criteria group decision making task, in which the uni-modal detectors are coordinated for a consensus final detection decision, based on their inter-relations. Specifically, we mine the complementariness and redundancy inter-relation of uni-modal detectors using the Ordered Weighted Average (OWA) operator. The `or-ness' measure in OWA models the inter-relation of uni-modal detectors as combination of pure complementariness and pure redundancy. The resulting weights of OWA can then yield a consensus fusion, by optimally leveraging the decisions of uni-modal detectors. The experiments on TRECVID 07 dataset show that the proposed OWA aggregation operator can significantly outperform other fusion methods, by achieving a state-of-art MAP of 0.132.