基于哈希算法及生成对抗网络的图像检索

Image Retrieval Based on Hash Method and Generative Adversarial Networks

哈希方法是大规模图像检索中生成哈希码的有效方法。现有的哈希方法首先提取描述图像整体的特征,然后生成哈希码,但得到的哈希码并不精确。为了得到更精确的检索效果,提出一种新的检索方法,即采用卷积神经网络提取图像特征,利用哈希算法与输入二进制噪声变量的生成对抗网络共同学习图像的二进制哈希码,利用汉明距离对图像进行相似性比较,最后完成对图像数据的有效检索。在标准图像数据集上进行实验,结果证明,该方法可以有效地进行图像检索,相比现有的哈希方法,该方法的检索性能也得到了提升。

Hash method is an effective method for generating hash codes in large-scale image retrieval. The current hash method extracts the characteristics of the whole image first and then generates hash code, but the obtained hash code is not very precise to obtain more precise retrieval effect. Aiming at this problem, we propose a new method. First, we use a convolutional neural network to extract image features. Then, we adopt hash algorithm and generative adversarial network of input binary noise variable to learn image binary hash code, and carry out image similarity comparison by using hamming distance. Finally, we complete the effective retrieval of image data. Experiments on standard image data sets show that this method can effectively perform image retrieval, and the retrieval performance is improved than other methods.