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Ning Chen,Wanggen Wan 한국전자통신연구원 2010 ETRI Journal Vol.32 No.2
In this letter, we present a new speech hash function based on the non-negative matrix factorization (NMF) of linear prediction coefficients (LPCs). First, linear prediction analysis is applied to the speech to obtain its LPCs, which represent the frequency shaping attributes of the vocal tract. Then, the NMF is performed on the LPCs to capture the speech’s local feature, which is then used for hash vector generation. Experimental results demonstrate the effectiveness of the proposed hash function in terms of discrimination and robustness against various types of content preserving signal processing manipulations.
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Identification of Key Nodes in Microblog Networks
Jing Lu,Wanggen Wan 한국전자통신연구원 2016 ETRI Journal Vol.38 No.1
A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user’s direct appeal, a user’s influence region, and a user’s global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms — PageRank, Betweeness Centrality, Closeness Centrality, Out-degree — using a new tweets propagation model — the Ignorants-Spreaders-Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.
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Abnormal Event Detection Based on Saliency Information
Zhijun Fang,Fengchang Fei,Yuming Fang,Lei Shu,Wanggen Wan 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.9
Abnormal event detection is a challenging task in video analysis. In this paper, we propose a new abnormal event detection algorithm for surveillance videos. It is well accepted that human eyes are extremely sensitive to abnormal events and they can quickly pay attention to the locations of these abnormal events in visual scenes. Thus, the characteristics of the Human Visual System (HVS) can be used for abnormal event detection. By exploiting the characteristics of the HVS, we propose an abnormal event detection algorithm based on saliency information. Firstly, the saliency information is extracted from video frames based on the feature contrast. The motion information of video frames is calculated by the multi-scale histogram optical flow (MHOF). Based on the features of saliency information and MHOF, the Support Vector Machine (SVM) is used to train and predict the abnormal events in visual scenes. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones over the public video database.
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