用于高效图像检索的多编码哈希方法
Multiple Code Hashing for Efficient Image Retrieval
AI总结：
本文提出多编码哈希（MCH）框架，通过深度强化学习为每张图像学习多个区域哈希码，以解决单哈希码在复杂语义下哈希桶搜索效率低的问题，显著提升图像检索性能。
摘要：
由于哈希方法具有低存储成本和快速查询速度的优势，其已被广泛应用于大规模图像检索任务中。哈希桶搜索返回给定汉明半径内与每个查询相近的数据点，从而能够以常数或亚线性的时间代价实现搜索。然而，现有的哈希方法在复杂场景下无法为哈希桶搜索取得令人满意的检索性能，因为它们仅为每张图像学习一个哈希码。更具体地说，通过使用一个哈希码来表示一张图像，当图像的语义信息较为复杂时，现有方法可能无法将相似图像对放入与查询之间汉明距离较小的桶中。因此，基于所学到的编码，检索相似图像时需要访问大量哈希桶。这将降低哈希桶搜索的效率。本文提出了一种新的哈希框架，称为多编码哈希（MCH），以提升哈希桶搜索的性能。MCH 的主要思想是为每张图像学习多个哈希码，每个编码表示图像的不同区域。此外，我们提出了一种深度强化学习算法来学习 MCH 中的参数。据我们所知，这是首个在图像检索中为每张图像学习多个哈希码的工作。实验表明，与仅为每张图像学习一个哈希码的现有方法相比，MCH 能够在哈希桶搜索中取得显著提升。
英文摘要：
Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
