基于栈式自动编码的图像哈希算法

Image Hashing algorithm based on stacked autoencoder

随着网络图像的快速发展,在大型图像检索系统中哈希算法成为近似最近邻查询算法的研究重点。本文提出一种基于深度模型的哈希算法—深度哈希。通过深度卷积神经网络提取的图像高维全局特征,用栈式自动编码器对特征进行无监督学习得到二进制哈希编码,利用图像标签语义相似性对栈式自动编码器的参数进行微调,最后用汉明距离来计算图像的相似性。本文提出的深度哈希在图像检索中取得了较好的结果。

With the rapid development of network in the large image,image hashing algorithm has attracted interests as an approach of approximate nearest neighbor algorithm in the image retrieval system.In this paper,we proposed the deep hash which based on deep learning models.The high dimensional global are extracted by deep convolutional neural network,then using stack autoencoder to get the parameters of the models by unsupervised learning to get the binary hash code.Finally using the hamming distance to compute the similarity of the images.The deephash proves the better results in image retrieval.