Abstract
Discovering the visual representation(s) of a tourism destination is a challenging problem because it should be highly discriminating and frequently appeared in the travel photos of this destination. To address this issue, we propose a deep relevance feature clustering method (DRFC). To ensure the discrimination, DRFC uses layer-wise relevance propagvel feature maps to locate the region that contributes the most to network prediction. For frequency, DRFC clusters the extracted relevance features in a feature space according to their density, and selects highly dense instances for the visual representation. The experiments 100K photos of 20 tourism destinations show that DRFC can discover the discriminating and frequent visual representation, and outperforms the state-of-the-art methods.
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Acknowledgments
This work is supported by the Science and Technology Plan of Xi’an (20191122015KYPT011JC013) and the Fundamental Research Funds of the Central Universities of China (No. JX18001).
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Wang, Q., Zhu, Z., Liang, X., Shi, H., Cao, P. (2020). Deep Relevance Feature Clustering for Discovering Visual Representation of Tourism Destination. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12307. Springer, Cham. https://doi.org/10.1007/978-3-030-60636-7_28
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