Unsupervised Multi-Hashing for Image Retrieval in Non-stationary Environments
Unsupervised Multi-Hashing for Image Retrieval in Non-stationary Environments
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DOI：
10.1109/icaci58115.2023.10146177
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Yi Yang;Qihua Li;Xing Tian;Wing W. Y. Ng;Hui Wang;J. Kittler;M. Gales;Rob Cooper
中科院分区：
文献类型：
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作者：
Yi Yang;Qihua Li;Xing Tian;Wing W. Y. Ng;Hui Wang;J. Kittler;M. Gales;Rob Cooper
哈希方法有助于大规模数据集的快速检索，这对现实世界的图像检索非常重要。现实世界中不断产生新的数据，这可能导致概念漂移和检索结果不准确。为了解决这个问题，提出了非平稳环境下的哈希方法。然而，大多数非平稳数据环境中的哈希方法都是有监督的。在实际应用中，特别是在非平稳数据环境中，很难获得准确的数据标签。因此，我们提出了非平稳环境下的无监督多哈希（UMH）方法。因此，在UMH中，当出现新的数据块时，将训练一组哈希函数并将其添加到保存的哈希函数集列表中。然后，保留多组不同权值的哈希函数，保证新旧数据的相似性信息都被适应。在两个真实图像数据集上的实验表明，UMH在非平稳环境下的检索性能优于其他比较方法。
Hashing methods help retrieve swiftly in large-scale dataset, which is important for real-world image retrieval. New data is produced continually in the real world which may cause concept drift and inaccurate retrieval results. To address this issue, hashing methods in non-stationary environments are proposed. However, most hashing methods in non-stationary data environments are supervised. In practice, it is hard to get exact labels of data especially in non-stationary data environments. Therefore, we propose the unsupervised multi-hashing (UMH) method for unsupervised image retrieval in non-stationary environments. Thus, in the UMH, a set of hash functions is trained and added to the kept list of hash functions sets when a new data chunk occurs. Then, multiple sets of hash functions are kept with different weights to guarantee that similarity information in old and new data are both adapted. Experiments on two real-world image datasets show that the UMH yields better retrieval performance in non-stationary environments than other comparative methods.