Unsupervised deep video hashing via balanced code for large-scale video retrieval
Unsupervised deep video hashing via balanced code for large-scale video retrieval
复制标题
通过平衡代码进行无监督深度视频哈希，用于大规模视频检索
DOI：
10.1109/tip.2018.2882155
复制
发表时间：
2019
影响因子：
10.6
通讯作者：
Shao Ling
中科院分区：
文献类型：
--
作者：
Wu Gengshen;Han Jungong;Guo Yuchen;Liu Li;Ding Guiguang;Ni Qiang;Shao Ling
本文提出了一种深度哈希框架，即无监督深度视频哈希（UDVH），用于大规模视频相似性搜索，旨在学习紧凑而有效的二进制代码。我们的UDVH产生的散列代码在自学的方式，通过联合集成的歧视性视频表示与最佳代码学习，其中采用了一种有效的交替方法来优化目标函数。与大多数现有视频哈希方法的主要区别在于：1）UDVH是一种无监督散列方法，其通过协同利用特征聚类和特别设计的二进制化来生成散列码，其中原始邻域结构保留在二进制空间中，以及2）开发特定旋转并将其应用于视频特征，使得每个维度的方差可以平衡，从而便于随后的量化步骤。在三个流行的视频数据集上进行的大量实验表明，UDVH在各种评价指标方面都远远优于现有技术，这使得它在现实世界的应用中非常实用。
This paper proposes a deep hashing framework, namely, unsupervised deep video hashing (UDVH), for large-scale video similarity search with the aim to learn compact yet effective binary codes. Our UDVH produces the hash codes in a self-taught manner by jointly integrating discriminative video representation with optimal code learning, where an efficient alternating approach is adopted to optimize the objective function. The key differences from most existing video hashing methods lie in: 1) UDVH is an unsupervised hashing method that generates hash codes by cooperatively utilizing feature clustering and a specifically designed binarization with the original neighborhood structure preserved in the binary space and 2) a specific rotation is developed and applied onto video features such that the variance of each dimension can be balanced, thus facilitating the subsequent quantization step. Extensive experiments performed on three popular video datasets show that the UDVH is overwhelmingly better than the state of the arts in terms of various evaluation metrics, which makes it practical in real-world applications.