无监督视频对象发现的主题-运动模型

David Liu, Tsuhan Chen
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引用次数: 47

摘要

近年来,词袋表示在物体识别领域引起了广泛的关注。基于词袋表示的主题模型,如概率潜在语义分析(PLSA)已被应用于静止图像的无监督对象发现。在本文中,我们将主题模型从静态图像扩展到运动视频,并集成了一个时间模型。我们提出了一个新的时空框架,该框架使用主题模型进行外观建模,并使用概率数据关联(PDA)滤波器进行运动建模。空间和时间模型紧密结合,运动模糊可以通过外观来解决,外观模糊可以通过运动来解决。我们展示了有希望的结果,不能通过外观或运动建模单独实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Topic-Motion Model for Unsupervised Video Object Discovery
The bag-of-words representation has attracted a lot of attention recently in the field of object recognition. Based on the bag-of-words representation, topic models such as probabilistic latent semantic analysis (PLSA) have been applied to unsupervised object discovery in still images. In this paper, we extend topic models from still images to motion videos with the integration of a temporal model. We propose a novel spatial-temporal framework that uses topic models for appearance modeling, and the probabilistic data association (PDA) filter for motion modeling. The spatial and temporal models are tightly integrated so that motion ambiguities can be resolved by appearance, and appearance ambiguities can be resolved by motion. We show promising results that cannot be achieved by appearance or motion modeling alone.
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