基于改进哈希算法的图像检索方法

当前主流图像检索技术所采用的传统视觉特征编码缺少足够的学习能力,影响学习得到的特征表达能力。此外,由于视觉特征维数高,会消耗大量的内存,因此降低了图像检索的性能。文中基于深度卷积神经网络与改进的哈希算法,提出并设计了一种端到端训练方式的图像检索方法。该方法将卷积神经网络提取的高层特征和哈希函数相结合,学习到具有足够表达能力的哈希特征,从而在低维汉明空间中完成对图像数据的大规模检索。在两个常用数据集上的实验结果表明,所提出的哈希图像检索方法的检索性能优于当前的一些主流方法。

An Image Retrieval Algorithm Based on Improved Hashing Method

The coding methods of the traditional visual features adopted in current image retrieval approaches lack sufficient learning ability and have no strong feature expression ability.In addition, due to the high dimensionality of visual features, a large amount of memory is consumed, thus reducing the performance of image retrieval. In this paper, an image retrieval algorithm with end-to-end training based on deep and improved hashing method was proposed and designed. The proposed algorithm combined the high-level features extracted by CNN with Hash function and learned Hash codes with expression ability to perform large-scale image retrieval in low-dimensional Hamming space. The experimental results on two main datasets showed that the retrieval performance of the proposed method was superior to that of some state-of-the-art ones.