关键词: 图像检索, 卷积神经网络, 主成分分析, 深层特征

Abstract: Due to the arriving of the Internet plus era, the number of online images have increased dramatically, and content-based image retrieval has attracted a lot of attention. Traditional retrieval methods are inefficient because of the low expression ability, which is not conducive to large-scale image retrieval. So, a new image retrieval algorithm based on convolutional neural network is proposed. A new end-to-end convolutional neural network framework is designed which learns probability based semantic-level similarity and feature-level similarity simultaneously. The principal component analysis method is introduced to reduce the dimensions of deep features and reduce the loss of information. The distance function is used to calculate the distance between the target image and the database image to realize the retrieval. Experimental results of ImageNet-1000 and Oxford 5K datasets indicate that the expression ability of visual feature is effectively improved and the image retrieval performance is substantially boosted compared with the contrast methods．

Key words: image retrieval, convolutional neural network, principal component analysis, deep features