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This paper proposes an automatic semantic video content indexing and retrieval system based on fusing various low level visual and shape descriptors. Extracted features from region and sub-image blocks segmentation of video shots key-frames are described via IVSM signature (Image Vector Space Model) in order to have a compact and efficient description of the content. Static feature fusion based on...
Feature ranking, due to its simplicity and computational efficiency, is a widely used dimensionality reduction technique, especially for large dataset where other methods are computationally too expensive. Conventionally feature ranking is done based on a single ranking criterion. One drawback associated with the conventional, single-criterion ranking is that the ranking order of the features is very...
In this paper, several classification methods are presented and a fusion structure is included to improve the final classification performance. The definition of "layer" and the method to create it are then introduced. Based on "layer", a multiple level change detection algorithm is proposed, which gives the details of the changes in each region and is demonstrated to be an easy,...
Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training...
Most predictive modeling in information fusion is performed using ensembles. When designing ensembles, the prevailing opinion is that base classifier diversity is vital for how well the ensemble will generalize to new observations. Unfortunately, the key term diversity is not uniquely defined, leading to several diversity measures and many methods for diversity creation. In addition, no specific diversity...
This paper reviews multiple criteria classification methods (or multi-criteria classifiers), particularly those based on concordance/discordance concepts. The concordance refers to an aggregated metric indicating the truthfulness of a proposition according to a coalition of criteria. The discordance is an aggregated metric representing the strength of the opposition coalition to the truthfulness of...
This paper considers the accuracy of state estimation based on classification using Bayesian networks. It presents a method to localize network fragments that (i) are in a particular (rare) case responsible for a potential misclassification, or (ii) contain modeling errors that consistently cause misclassifications, even in common cases. We derive an algorithm that, within such fragments, can localize...
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