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We address the problem of fine-grained image recognition using user click data, wherein each image is represented as a semantical query-click feature vector. Usually, the query set obtained from search engines is large-scale and redundant, making the click feature be high-dimensional and sparse. We propose a novel query modeling approach to merge semantically similar queries, and construct a compact...
Human action recognition has been well studied recently, but recognizing the activities of more than three persons remains a challenging task. In this paper, we propose a motion trajectory based method to classify human group activities. Gaussian Processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability of people's activities in group...
For broadcast sports video, score information is an effective mid-level representation to facilitate high-level video content analysis. In this paper, we propose a real-time approach to detect score region and recognize scores in broadcast basketball video. First, score region is automatically detected using frame difference and texture information without any prior knowledge. Then, score digit is...
Shot detection is the first stage of video analysis. In this paper, we present a machine learning based shot detection approach using hidden Markov models (HMMs), in which both the color and shape clues are utilized. Its advantages are twofold. First, the temporal characteristics of different shot transitions are exploited and an HMM is constructed for each type of shot transitions, including cut...
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