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  • Paisitkriangkrai, S; Shen, C; Zhang, Jian (NA, 2011)
    There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted ...
  • Chi, Lianhua; Feng, Yucai; Chi, Hehua; Huang, Ying (IEEE, 2012)
    This paper proposes a new face image recognition method based on time series motifs. Time series motifs are previously unknown, frequently occurring patterns in time series. In this paper, we make full use of time series ...
  • Wang Lin; He Xiangjian; Du Ruo; Jia Wenjing; Wu Qiang; Yeh Wei-Chang (Springer Berlin Heildelberg New York, 2011)
    In our earlier work, we have proposed an HVF (Histogram Variance Face) approach and proved its effectiveness for facial expression recognition. In this paper, we extend the HVF approach and present a novel approach for ...
  • Wang, Lin; Du, Ruo; Jia, Wenjing; Wu, Qiang; Yeh, Wei-Chang; He, Sean (Springer, 2011)
    In our earlier work, we have proposed an HVF (Histogram Variance Face) approach and proved its effectiveness for facial expression recognition. In this paper, we extend the HVF approach and present a novel approach for ...
  • Hussain, Omar; Chang, Elizabeth; Soh, Ben; Hussain, Farookh; Dillon, Tharam (Computer Associates, 2005)
    Decentralized transactions are increasingly becoming popular. These transactions resemble the early forms of the internet and in many ways are regarded as the next generation of the internet. The result will be that this ...
  • Lu, Jie; Zhang, Guangquan; Hao, Zhifeng; Wen, W; Yang, Xiao (Springer-Verlag, 2006)
    A fast data preprocessing procedure (FDPP) for support vector regression (SVR) is proposed in this paper. In the presented method, the dataset is firstly divided into several subsets and then K-means clustering is implemented ...
  • Yuwono, Mitchell; Su, Steven; Moulton, Bruce; Nguyen, Hung (IEEE, 2012)
    Data clustering is a process where a set of data points is divided into groups of similar points. Recent approaches for data clustering have seen the development of unsupervised learning algorithms based on Particle Swarm ...
  • Khosravi, Anas; Lu, Jie (Prokom Softwares, 2006)
  • Lowe, David; Mujkanovic, Amir; Miorandi, Daniel; Yamamoto, Lidia (Springer-Verlag Berlin Heidelberg, 2010)
    In previous work the authors have described an approach for building distributed selfa??healing systems a?? referred to as EmbryoWare a?? that, in analogy to Embryonics in hardware, is inspired by cellular development and ...
  • Tan, Chek Tien; Rosser, Daniel; Bakkes, Ing; Pisan, Yusuf (ACM, 2012)
    Current quantitative methods of measuring player experience in games are mostly intrusive to play and less suited to natural, non-laboratory play environments. This paper presents an initial study to validate the ...
  • Li, Zelin; Zhang, Jian; Wu, Qiang; Geers, Glen (IEEE Computer Society, 2010)
    Feature enhancement in an image is to reinforce some exacted features so that it can be used for object classification and detection. As the thermal image is lack of texture and colorful information, the techniques for ...
  • Bian, Wei; Li, Jing; Tao, Dacheng (Springer-Verlag Berlin, 2010)
    Mitchell et al. [9] demonstrated that support vector machines (SVM) are effective to classify the cognitive state of a human subject based on fRMI images observed over a single time interval. However, the direct use of ...
  • Zhang, Luming; Song, Mingli; Bian, Wei; Tao, Dacheng; Liu, Xiao; Bu, Jiajun; Chen, Chun (Springer-Verlag Berlin / Heidelberg, 2011)
    Utilizing multimodal features to describe multimedia data is a natural way for accurate pattern recognition. However, how to deal with the complex relationships caused by the tremendous multimodal features and the curse ...
  • Liu, Huawei; Wu, Xindong; Zhang, Shichao (ACM, 2011)
    One of the challenges in data mining is the dimensionality of data, which is often very high and prevalent in many domains, such as text categorization and bio-informatics. The high-dimensionality of data may bring many ...
  • Kamal, Abu; Zhu, Xingquan; Pandya, Abhijit; Hsu, Sam (IEEE Computer Society, 2009)
    Feature selection concerns the problem of selecting a number of important features (w.r.t. the class labels) in order to build accurate prediction models. Traditional feature selection methods, however, fail to take the ...
  • Xu, Zhongwen; Yang, Yi; Tsang, Ivor; Hauptmann, Alexander; Sebe, Nicu (IEEE, 2013)
    Fusion of multiple features can boost the performance of large-scale visual classification and detection tasks like TRECVID Multimedia Event Detection (MED) competition [1]. In this paper, we propose a novel feature fusion ...
  • Chang, Lijun; Yu, Jeffrey; Qin, Lu; Zhu, Yuanyuan; Wang, Haixun (ACM, 2011)
    Social and information networks have been extensively studied over years. In this paper, we concentrate ourselves on a large information network that is composed of entities and relationships, where entities are associated ...
  • Ding, Bolin; Yu, Jeffrey; Qin, Lu (ACM, 2008)
    The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to ...
  • Ding, Bolin; Yu, Jeffrey; Wang, Shan; Qin, Lu; Zhang, Xiao; Lin, Xuemin (IEEE, 2007)
    It is widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p1, ..., ...