位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴...位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴趣点推荐算法。首先,利用用户签到数据构建空间分布相似性模型;其次,引入削减因子,提高具有相同签到记录的用户权重;最后,线性融合用户及空间分布性相似性模型对Top-N兴趣点进行推荐,并进行实验验证。实验结果表明,该算法有效提高了兴趣点推荐的质量。展开更多
Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effect...Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effectively. In this paper we propose a structural similarity quality metric for videos based on a spatial-temporal visual attention model. This model acquires the motion attended region and the distortion attended region by computing the motion features and the distortion contrast. It mimics the visual attention shifting between the two attended regions and takes the burst of error into account by introducing the non-linear weighting fimctions to give a much higher weighting factor to the extremely damaged frames. The proposed metric based on the model renders the final object quality rating of the whole video sequence and is validated using the 50 Hz video sequences of Video Quality Experts Group Phase I test database.展开更多
文摘位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴趣点推荐算法。首先,利用用户签到数据构建空间分布相似性模型;其次,引入削减因子,提高具有相同签到记录的用户权重;最后,线性融合用户及空间分布性相似性模型对Top-N兴趣点进行推荐,并进行实验验证。实验结果表明,该算法有效提高了兴趣点推荐的质量。
文摘Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effectively. In this paper we propose a structural similarity quality metric for videos based on a spatial-temporal visual attention model. This model acquires the motion attended region and the distortion attended region by computing the motion features and the distortion contrast. It mimics the visual attention shifting between the two attended regions and takes the burst of error into account by introducing the non-linear weighting fimctions to give a much higher weighting factor to the extremely damaged frames. The proposed metric based on the model renders the final object quality rating of the whole video sequence and is validated using the 50 Hz video sequences of Video Quality Experts Group Phase I test database.
