IEEE Trans Image Process. 2017 Mar;26(3):1289-1299. doi: 10.1109/TIP.2017.2651390. Epub 2017 Jan 11.
Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications, such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices. In this paper, we propose a novel unsupervised hashing approach called min-cost ranking (MCR) specifically for learning powerful short binary codes (i.e., usually the code length shorter than 100 b) for scalable image retrieval tasks. By exploring the discriminative ability of each dimension of data, MCR can generate one bit binary code for each dimension and simultaneously rank the discriminative separability of each bit according to the proposed cost function. Only top-ranked bits with minimum cost-values are then selected and grouped together to compose the final salient binary codes. Extensive experimental results on large-scale retrieval demonstrate that MCR can achieve comparative performance as the state-of-the-art hashing algorithms but with significantly shorter codes, leading to much faster large-scale retrieval.
大规模视觉信息检索在这个大数据时代已经成为一个活跃的研究领域。最近，哈希/二进制编码算法被证明对于可扩展的检索应用是有效的。大多数现有的哈希方法需要相对较长的二进制代码（即超过数百位，有时甚至数千位）才能达到合理的检索精度。然而，对于一些现实和独特的应用，如在可穿戴或移动设备上，由于这些设备上的计算资源或带宽的限制，只能使用短的二进制代码来进行高效的图像检索。在本文中，我们提出了一种新的无监督哈希方法，称为最小代价排序（MCR），专门用于学习强大的短二进制代码（即通常长度短于 100 位），以用于可扩展的图像检索任务。通过探索数据每个维度的判别能力，MCR 可以为每个维度生成一位二进制代码，并根据提出的代价函数同时对每个位的判别可分离性进行排序。然后只选择具有最小代价值的前几位，并将它们分组在一起，组成最终的显著二进制代码。在大规模检索上的广泛实验结果表明，MCR 可以实现与最先进的哈希算法相当的性能，但具有明显更短的代码，从而实现更快的大规模检索。
