关键词: 图像检索, 卷积神经网络, 池化, 哈希编码

Abstract:

Recently, many scholars have done research on the methods of image processing by combining deep neural networks. Compared with the traditional hand-crafted feature, the feature extracted by deep convolutional networks is more accurate and better. Therefore, this paper proposes a deep network architecture combined with convolutional neural networks and Hashing method for large-scale image retrieval. On the one hand, in order to preserve semantic similarity between binary Hash codes as much as possible, this paper introduces the multi-task learning that the similarity information and the classified information are used to train the network. Moreover, according to the information entropy theory, Hash coding is maintained as evenly distributed as possible to increase the amount of information during the training process. On the other hand, this paper proposes a multi-scale fusion pooling method (MSFP), which can fuse the regional information of various scales, and improve the retrieval performance. Meanwhile, the network parameters are obviously reduced by the pooling strategy. Extensive experiments on three large scale datasets SVHN, CIFAR-10 and NUS-WIDE show the improved performance of this method compared with the state-of-the-arts.

Key words: image retrieval, convolutional neural networks, pooling, Hashing