Abstract
In our previous study, to address the prevalent issue of scarcity of labeled remote sensing (RS) images, we proposed a novel RS image retrieval model called similarity based Siamese convolutional neural network (SBS-CNN). SBS-CNN accomplishes unsupervised transfer learning with the help of several pretrained CNNs called CNN experts, which compute similarities for RS image pairs and provide the basis for unsupervised training. In this paper, we further investigate two problems: 1) Is CNN experts’ “opinion” on image similarity the same as humans’? 2) can human opinion help increase retrieval performance? To this end, we propose an effective method of collecting human views, and incorporate them into unsupervised training process by introducing an adaptive “push-pull” mechanism into triplet networks. Experimental results reveal that 1) CNN experts’ views on similarity are quite different from humans’, however, two kinds of views are strongly positively correlated; 2) human views can greatly improve retrieval performance.
Supported by National Natural Science Foundation of China under Grant 61673184.
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Liu, Y., Chen, C., Han, Z., Liu, Y., Ding, L. (2020). Incorporating Human Views into Unsupervised Deep Transfer Learning for Remote Sensing Image Retrieval. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_34
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