论文标题
基于随机VLAD的深度散列以进行有效的图像检索
Random VLAD based Deep Hashing for Efficient Image Retrieval
论文作者
论文摘要
图像哈希算法会生成紧凑的二进制表示，可以快速通过锤距匹配，从而成为大规模图像检索的有效解决方案。本文提出了RV-SSDH，这是一种深层图像哈希算法，该算法将经典的VLAD（本地汇总描述符的向量）结合到神经网络中。具体而言，通过将随机VLAD层与潜在的哈希层耦合通过转换层形成新型的神经网络组件。该组件可以与卷积层结合使用，以实现哈希算法。我们将RV-SSDH实现为一种优口算法，可以通过最大程度地减少分类错误和量化损失来有效训练。全面的实验表明，这种新的建筑的表现明显优于Netvlad和SSDH等基线，并在最先进的面前提供了具有成本效益的权衡。另外，提出的随机VLAD层导致令人满意的精度，因此显示出有希望的电位，作为NetVlad的替代方案。
Image hash algorithms generate compact binary representations that can be quickly matched by Hamming distance, thus become an efficient solution for large-scale image retrieval. This paper proposes RV-SSDH, a deep image hash algorithm that incorporates the classical VLAD (vector of locally aggregated descriptors) architecture into neural networks. Specifically, a novel neural network component is formed by coupling a random VLAD layer with a latent hash layer through a transform layer. This component can be combined with convolutional layers to realize a hash algorithm. We implement RV-SSDH as a point-wise algorithm that can be efficiently trained by minimizing classification error and quantization loss. Comprehensive experiments show this new architecture significantly outperforms baselines such as NetVLAD and SSDH, and offers a cost-effective trade-off in the state-of-the-art. In addition, the proposed random VLAD layer leads to satisfactory accuracy with low complexity, thus shows promising potentials as an alternative to NetVLAD.
