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RISC-Net : rotation invariant siamese convolution network for floor plan image retrieval

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Abstract

A floor plan represents the blue print of a building. Organizing a massive set of such floor plans and accessing them based on similarity is challenging for any architect. During the digitization process printed floor plan images are rotated slightly by a small degree of angle. Handcrafted feature-based methods proposed in the literature fail to generalize on such scenarios efficiently. In this paper we propose a deep learning-based model, Rotation Invariant Siamese Convolution Network (RISC-Net), which is able to retrieve similar floor plan images from the dataset, even in the presence of rotation. Uniqueness of RISC-Net is the ability to handle scan-time rotation both in the query as well as the images in the database. The proposed method is trained and evaluated on a publicly available floor plan image ROBIN dataset and achieved the best retrieval results 79% as compared to the state-of-the-art methods proposed in the same problem domain.

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Acknowledgements

This work is partially supported by Science and Engineering Research Board, India, under the project id ECR/2016/000953.

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Authors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Atharva Kalsekar, Rasika Khade & Krupa Jariwala

  2. Indian Institute of Technology, Jodhpur, India

    Chiranjoy Chattopadhyay

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  1. Atharva Kalsekar
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  2. Rasika Khade
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  3. Krupa Jariwala
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  4. Chiranjoy Chattopadhyay
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Correspondence to Chiranjoy Chattopadhyay.

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Kalsekar, A., Khade, R., Jariwala, K. et al. RISC-Net : rotation invariant siamese convolution network for floor plan image retrieval. Multimed Tools Appl 81, 41199–41223 (2022). https://doi.org/10.1007/s11042-022-13124-3

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  • DOI: https://doi.org/10.1007/s11042-022-13124-3

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