In content-based image retrieval (CBIR), the support vector machine (SVM) based relevance feedback is studied extensively to narrow the gap between low-level image feature and high-level semantic concept. Despite the ...
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In content-based image retrieval (CBIR), the support vector machine (SVM) based relevance feedback is studied extensively to narrow the gap between low-level image feature and high-level semantic concept. Despite the success, for conventional SVM relevance feedback, the retrieval performance is actually worse when the number of labeled positive feedback samples is small. To overcome this limitation, a SVM classifier combination for relevance feedback content-based image retrieval using expectation-maximization (em) parameterestimation is proposed. Firstly, we introduce the asymmetric bagging SVM to improve the stability of SVM classifiers and the balance in the training. Then, the random subspace SVM is used to overcome the overfitting problem. Finally, we combine the asymmetric bagging SVM and the random subspace SVM using em parameter estimation to form an integrated SVM as a hypothesized solution to the overall image retrieval problem, which can further improve the relevance feedback performance. Experiments on large databases show that the proposed algorithms are significantly more effective than the state-of-the-art approaches. (C) 2010 Elsevier B.V. All rights reserved.
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents th...
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This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation - free flowing, congested or faulty - making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (em) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an em parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator.
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