Video Concept Detection Based on Multiple Features and Classifiers Fusion

The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systelrtically investigated, and kernelbased learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systen on both classifier-level and kernel-level fusion that contribute to a more robust system Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision （MAP） score is rmch better than the benchmark perforrmnce, which proves that our concepts detection engine develops a generic model and perforrrs well on both object and scene type concepts.

Multiple hypergraph ranking for video concept detection

This paper tackles the problem of video concept detection using the multi-modality fusion method. Motivated by multi-view learning algorithms, multi-modality features of videos can be represented by multiple graphs. And the graph-based semi-supervised learning methods can be extended to multiple graphs to predict the semantic labels for unlabeled video data. However, traditional graphs represent only homogeneous pairwise linking relations, and therefore the high-order correlations inherent in videos, such as high-order visual similarities, are ignored. In this paper we represent heterogeneous features by multiple hypergraphs and then the high-order correlated samples can be associated with hyperedges. Furthermore, the multi-hypergraph ranking (MHR) algorithm is proposed by defining Markov random walk on each hypergraph and then forming the mixture Markov chains so as to perform transductive learning in multiple hypergraphs. In experiments on the TRECVID dataset, a triple-hypergraph consisting of visual, textual features and multiple labeled tags is constructed to predict concept labels for unlabeled video shots by the MHR framework. Experimental results show that our approach is effective.

LDM-Satellite:A New Scheme for Packet Loss Classification over LEO Satellite Network

The packet loss classification has always been a hot and difficult issue in TCP congestion control research.Compared with the terrestrial network,the probability of packet loss in LEO satellite network increases dramatically.What’s more,the problem of concept drifting is also more serious,which greatly affects the accuracy of the loss classification model.In this paper,we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks,named LDM-Satellite,which consists of three modules:concept drift detection,lost packet cache and hybrid integration classification.As far,this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks.We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier.And a new weight algorithm for these classifiers is given.In ns-2 simulation,LDM-Satellite has a better AUC(0.9885)than the single-model machine learning classification algorithms.The accuracy of loss classification even exceeds 98%,higher than traditional TCP protocols.Moreover,compared with the existing protocols used for satellite networks,LDM-Satellite not only improves the throughput rate but also has good fairness.