An adaptive method based on fuzzy logic and AdaBoost for exhibition of museum collections

Nianci Wang; Ke Zhao

doi:10.1117/12.2681280

8 June 2023 An adaptive method based on fuzzy logic and AdaBoost for exhibition of museum collections

Nianci Wang, Ke Zhao

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127075A (2023) https://doi.org/10.1117/12.2681280
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

The role of museums in science popularization is receiving unprecedented attention. Among them, the Electronic Science and Technology Museum has recorded the history of the electronic industry. When designing the exhibition of the Electronic Science and Technology Museum, the classification and deployment of collection types are very important. However, most of the existing classification methods of museum collection types rely on simple physical information, such as the production time and composition of the collection, and lack scientific identification and classification methods for electronic instrument collections of the same era and technology. In this paper, we propose a fast collection type recognition method based on fuzzy logic and machine learning model. First, we use fuzzy logic to expand the attribute dimension of the collection and enrich the attribute information of the collection. Subsequently, we use the Adaboost model to realize rapid recognition of collection types. Using the determined type of collection information to train the Adaboost model, so that it can quickly and efficiently identify the unified type of collection. The simulation results show that the proposed method can further improve the recognition accuracy of electronic instrument collections, and can well serve the museum exhibition design.

Citation Download Citation

Nianci Wang and Ke Zhao "An adaptive method based on fuzzy logic and AdaBoost for exhibition of museum collections", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127075A (8 June 2023); https://doi.org/10.1117/12.2681280

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