自动相关性反馈视频检索

P. Muneesawang, L. Guan
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引用次数: 17

摘要

提出了一种提高视频数据库检索精度的自动关联反馈方法。我们首先展示了一个基于模板频率模型(TFM)的表示,该模型允许充分利用时间维度。然后,我们将TFM与自训练神经网络结构相结合,以自适应地捕获视频序列中不同程度的视觉重要性。在这种自动相关反馈方法中,信号的前向和后向传播是提高检索精度的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic relevance feedback for video retrieval
This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to adaptively capture different degrees of visual importance in a video sequence. Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.
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