Building Plan Reconstruction based on codebook and keyblock framework
- DOI
- 10.2991/978-94-6463-413-6_5How to use a DOI?
- Keywords
- Keyblock; Vector Quantization; Codebook; Generalized Lloyd Algorithm; Building Plan Reconstruction
- Abstract
These days, one of the biggest challenges facing pattern recognition research is building plan recognition. The findings of an exploratory investigation on keyblock framework-based building plan recognition are presented in this paper. This study also aims to demonstrate the universal applicability of the keyblock framework for CBIRS. Using the keyblock features for encoding and decoding building plan images, a codebook is constructed in this research. This study uses the Generalized Lloyd Algorithm (GLA) to extract pertinent data and minimize block during codebook generation. A one-dimensional matrix representation of the encoding image is used, which is comparable to a set of keywords in a text retrieval system. To reconstruct and query the image using an example, we performed image decoding. The effectiveness of the keyblock framework on building plan reconstruction was assessed using the root-mean-square error of the rebuilt image as the performance metric.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - I Gusti Agung Gede Arya Kadyanan AU - Nyoman Gunantara AU - Ida Bagus Gede Manuaba AU - Komang Oka Saputra PY - 2024 DA - 2024/05/13 TI - Building Plan Reconstruction based on codebook and keyblock framework BT - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023) PB - Atlantis Press SP - 41 EP - 50 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-413-6_5 DO - 10.2991/978-94-6463-413-6_5 ID - Kadyanan2024 ER -