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In multiple-target tracking problem, data association technique plays an significant role. When targets move closely or crosswise, performances of conventional data association algorithms which use kinematic information only may be degraded. Actually, beside the kinematic information, sensors always can obtain feature information about the target, and incorporating the features into data association...
Classification in sparsely labeled networks is challenging to traditional neighborhood-based methods due to the lack of labeled neighbors. In this paper, we propose a novel behavior-based collective classification (BCC) method to improve the classification performance in sparsely labeled networks. In BCC, nodes’ behavior features are extracted and used to build latent relationships between labeled...
Given an image, our proposed model can extract its dominant high-level semantics information through low-level feature extraction and image classification. It contains 3 main parts: image segmentation, feature extraction and classification. To our knowledge, this is the first model that applies Color and Edge Directivity Descriptor (CEDD), a multiple feature extraction algorithm, into the high-level...
Non-negative Matrix Factorization (NMF) is a recently developed method for dimensionality reduction, feature extraction and data mining, etc. Currently no NMF algorithm holds both satisfactory efficiency for applications and enough ease of use. To improve the applicability of NMF, this paper proposes a new monotonic, fixed-point algorithm coined FastNMF by implementing least squares error-based non-negative...
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