Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature
首发时间:2016-06-13
Abstract:Video hashing is a common solution for content-based video retrieval by encoding high-dimensional feature vectors into short binary codes. Videos not only have spatial structure inside each frame but also have temporal correlation structure between frames, while the latter has been largely neglected by many existing methods. Therefore, in this paper we propose to perform video hashing by incorporating the temporal structure as well as the conventional spatial structure. Specifically, the spatial features of videos are obtained by utilizing Convolutional Neural Network (CNN), and the temporal features are established via Long-Short Term Memory (LSTM). The proposed spatio-temporal feature learning framework can be applied to many existing unsupervised hashing methods such as Iterative Quantization (ITQ), Spectral Hashing (SH), and others. Experimental results on the UCF-101 dataset indicate that by simultaneously employing the temporal features and spatial features, our hashing method is able to significantly improve the performance of existing methods which only deploy the spatial feature.
keywords: Video Hashing, Unsupervised Method, Spatio-temporal Feature
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基于时空特征的无监督视频哈希算法
摘要:视频哈希将高维的视频特征映射到低维的哈希编码,通常被用于基于内容的视频检索。视频的特征不仅仅包括每一帧图像内的空间特征,还包括了帧与帧之间的时域关联性特征,但是后者在视频哈希中往往被人们忽略。因此,本文提出利用视频的时空特征进行视频哈希,空间特征通过卷积神经网络(CNN)得到,时域特征通过时间递归神经网络(LSTM)得到,通过现有的无监督哈希方法,由视频的时空特性得到哈希编码。在公共数据集UCF-101上的结果表明,本文提出的方法能够有效地提高现有的视频哈希方算法的效果。
关键词: 视频哈希,无监督学习,时空特征
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