基于深度卷积神经网络的图像检索算法研究

为解决卷积神经网络在提取图像特征时所造成的特征信息损失，提高图像检索的准确率，提出了一种基于改进卷积神经网络LeNet-L的图像检索算法。首先，改进LeNet-5卷积神经网络结构，增加网络结构深度。然后，对深度卷积神经网络模型LeNet-L进行预训练，得到训练好的网络模型，进而提取出图像高层语义特征。最后，通过距离函数比较待检图像与图像库的相似度，得出相似图像。在Corel数据集上，与原模型以及传统的SVM主动学习图像检索方法相比，该图像检索方法有较高的准确性。经实验结果表明，改进后的卷积神经网络具有更好的检索效果。

Image retrieval based on deep convolutional neural network

To solve the problem that the loss of image feature information and improve the accuracy of image retrieval, when the Convolutional Neural Network(CNN) is used to extract the feature information of the image, this paper proposed an image retrieval algorithm based on improved Convolutional Neural Network LeNet-L. First, improved LeNet-5 Convolution Neural Network structure, increased depth of network structure. Then, the deep Convolutional Neural Network LeNet-L was pre-trained to extract the high-level semantic features. At last, the similar images was obtained by distance function between the image being retrieved and the one in image database. In Corel dataset, compared with the original model method and the traditional image retrieval method based on SVM and active learning, the proposed method has a higher accuracy. The experimental results show that the improved Convolutional Neural Network has a better retrieval effect.