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Abstract: Recently, image representation based on bag-of-visual-words (BoW) model has been popularly applied in image and vision domains. In BoW, a visual codebook of visual words is defined, usually by clustering local features, to represent any novel image with the occurrence of its contained visual words. Given a set of images, we argue that the significance of each image is determined by the significance of its contained visual words. Traditionally, the significances of visual words are defined by term frequency-inverse document frequency (tf-idf), which cannot necessarily capture the intrinsic visual context. In this paper, we propose a new scheme of latent visual context learning (LVCL). The visual context among images and visual words is formulated from latent semantic context and visual link graph analysis. With LVCL, the importance of visual words and images will be distinguished from each other, which will facilitate image level applications, such as image re-ranking and canonical image selection. We validate our approach on text-query based search results returned by Google Image. Experimental results demonstrate the effectiveness and potentials of our LVCL in applications of image re-ranking and canonical image selection, over the state-of-the-art approaches. [Copyright &y& Elsevier]

Abstract: For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach. [Copyright &y& Elsevier]

Abstract: Tactic analysis is an exciting and challenging problem in sport video analysis. The trajectories of ball and players convey rich tactic information, so the trajectory extraction and analysis are important for the soccer tactic analysis. Previous research on tactic analysis was generally based on finding object''s mosaic trajectory which does not capture the rich semantic information of the real-world trajectory. In this paper, we propose a complete framework to systematically analyze soccer tactics. Specifically, we first propose an efficient real-world trajectory extraction method based on field line detection. Secondly, we define and recognize six typical soccer attack patterns for tactic analysis. With experiments on user study, the proposed method can improve the tactic analysis in terms of the conciseness, clarity, and usability. [Copyright &y& Elsevier]