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Learning Short Binary Codes for Large-scale Image Retrieval

机译:学习短二进制码以进行大规模图像检索

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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.
机译:大规模的视觉信息检索已经成为这个大数据时代的活跃研究领域。最近,散列/二进制编码算法被证明对可伸缩检索应用有效。大多数现有的散列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),以实现合理的检索精度。但是,对于某些现实和独特的应用程序(例如在可穿戴设备或移动设备上),由于这些设备上计算资源或带宽的限制,仅短二进制代码可用于有效的图像检索。在本文中,我们提出了一种称为最小成本排名(MCR)的新颖无监督哈希方法,专门用于学习可伸缩图像检索任务的强大短二进制代码(即通常长度小于100 b的短二进制代码)。通过探索数据每个维度的区分能力,MCR可以为每个维度生成一个比特的二进制代码,并根据建议的成本函数同时对每个比特的区分性进行排序。然后仅选择具有最小成本值的排名靠前的位,并将其分组在一起以组成最终的显着二进制代码。关于大规模检索的大量实验结果表明,MCR可以作为最先进的哈希算法获得相当的性能,但代码明显更短,从而可以更快地进行大规模检索。

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