Abstract
This paper presents a Random Repeatable Network (RRN) which is an entirely unsupervised training framework for interest point detection. The existing learning based methods make tradeoff between the unsupervised level and the approximation degree to the objective of repeatability, while our RRN model trains a convolutional neural network whose loss function is directly based on point repeatability without relying on any initial interest point detector. In terms of point repeatability under perspective transform or illumination change, we propose a novel loss function with regularization term of repeatability, which is optimized by an effective iterative algorithm. Experiments demonstrate our model achieves better performance on test data compared to some state-of-the-art interest point detector.
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Acknowledgments
This research was partially supported by National Foundation of China under Grants 41371339 and The Fundamental Research Funds for the Central Universities No. 2017KFYXJJ179.
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Yan, P., Tan, Y. (2018). Random Repeatable Network: Unsupervised Learning to Detect Interest Point. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_37
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DOI: https://doi.org/10.1007/978-981-13-2829-9_37
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