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Wang Z.-M.,University of Science and Technology Beijing | Wang Z.-M.,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2015

Image quality has a strong impact on human visual information acquisition. It is a key but difficult task to evaluate the quality of a distorted image without a reference image. This paper reviews the main techniques of no-reference image quality assessment (IQA) developed during the past 20 years. Firstly, some technical indexes for IQA algorithm evaluation and several public IQA databases available on network are introduced. Then, various no-reference IQA algorithms are introduced, sorted and discussed in detail. At last, several non-distortion-specific no-reference IQA algorithms presented in recent years are tested and compared on a public database. The purpose of this paper is to provide an integrated and valuable reference for no-reference IQA research. Copyright © 2015 Acta Automatica Sinica. All rights reserved. Source


Chen Y.,University of Science and Technology Beijing | Chen Y.,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Sensors and Transducers | Year: 2014

At present, most of research works in mobile network focus on the network overhead of the known path which exists between the sender and the receiver. However, the trend of the current practical application demands is becoming increasingly distributed and decentralized. The Delay and Tolerant Network (DTN) just comes out of such background of the conflicts between them. The DTN could effectively eliminate the gap between the mobile network and the practical application demands. In this paper, a Self-Adaptive Context Aware Routing Protocol (SACARP) for the unicast communication in delay and tolerant networks is presented. Meanwhile, according to the real-time context information of DTN, the Kalman filter theory is introduced to predict the information state of mobility for the optional message ferrying node, and then gives the optimal selection strategy of the message ferrying nodes. The simulation experiments have shown that, compared to the familiar single-copy and multi-copy protocols, the SACARP proposed in this paper has better transmission performance and stability, especially when the network is free, the protocol would keep a good performance with fewer connections and less buffer space. © 2014 IFSA Publishing, S. L. Source


Qian W.,Nanchang University | Qian W.,Beijing Key Laboratory of Knowledge Engineering for Materials Science | Shu W.,East China Jiaotong University
Neurocomputing | Year: 2015

Feature selection is an important preprocessing step in machine learning and data mining, and feature criterion arises a key issue in the construction of feature selection algorithms. Mutual information is one of the widely used criteria in feature selection, which determines the relevance between features and target classes. Some mutual information-based feature selection algorithms have been extensively studied, but less effort has been made to investigate the feature selection issue in incomplete data. In this paper, combined with the tolerance information granules in rough sets, the mutual information criterion is provided for evaluating candidate features in incomplete data, which not only utilizes the largest mutual information with the target class but also takes into consideration the redundancy between selected features. We first validate the feasibility of the mutual information. Then an effective mutual information-based feature selection algorithm with forward greedy strategy is developed in incomplete data. To further accelerate the feature selection process, the selection of candidate features is implemented in a dwindling object set. Compared with existing feature selection algorithms, the experimental results on different real data sets show that the proposed algorithm is more effective for feature selection in incomplete data at most cases. © 2015 Elsevier B.V. Source


Li Y.,University of Science and Technology Beijing | Li Y.,Beijing Key Laboratory of Knowledge Engineering for Materials Science | Hu C.,University of Science and Technology Beijing | Minku L.L.,University of Birmingham | Zuo H.,University of Science and Technology Beijing
Genetic Programming and Evolvable Machines | Year: 2013

Learning aesthetic judgements is essential for reducing users' fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user's aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists' styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users' preferences. © 2013 Springer Science+Business Media New York. Source


Yu Y.,University of Science and Technology Beijing | Wang Q.,University of Science and Technology Beijing | Wang X.,Beijing Key Laboratory of Knowledge Engineering for Materials Science
China Communications | Year: 2013

The clustering of trajectories over huge volumes of streaming data has been recognized as critical for many modern applications. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line-segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi-tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experimental studies demonstrate that our algorithm achieves excellent effectiveness and high efficiency for continuous clustering on both synthetic and real streaming data, and the proposed query processing methods utilise average 90% less time than the naive query methods. © 2013 IEEE. Source

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