A Feature Encoding Based on Low Space Complexity Codebook Called Fuzzy Codebook for Image Recognition

Shinomiya, Yuki; Hoshino, Yukinobu

doi:10.1007/s40815-018-0568-2

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature extraction of the research was performed using the scale-invariant feature transform (SIFT) descriptor, and they scored an accuracy of 91.56% for the classification. Furthermore, the SIFT-BOF-based image recognition system is developed by Yuki et al, 13 and they propose a fuzzy code book to reduce computational complexity. Furthermore, Raj Kumar et al 14 used the SURF feature descriptor to generate a dataset of 1000 samples to identify Fungal Blast disease in rice seeds.…”

Section: Related Workmentioning

confidence: 99%

An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm

Rathnayake

Dang

Miyazaki

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

View full text Add to dashboard Cite

It is a well-known fact that the quality of a seed highly impacts the germination of a rice seed. The age of the seed is one of the primary key points in assessing the seed quality. Therefore, this study aims to develop an AI-based machine-learning model to classify age-wise rice seeds. This study employs the SURF-BOF-based Cascaded-ANFIS algorithm for the implementation of the classifier. The proposed model performances were compared to the VGG16. Moreover, this research contributes a novel Japanese rice seed dataset to the scientific community. Furthermore, a 10-Fold cross-validation is performed to evaluate the robustness of the novel approach. The K-fold cross-validation's mean accuracy confirmed the proposed algorithm's higher robustness in the age-wise classification of rice seeds. Nevertheless, the results were evaluated using the confusion matrix and metrics such as precision, recall, and F1-Score. The Accuracy of Akitakomachi, Koshihikari, Yandao-8, and rice variety classification is 99%, 99%, 92%, and 97%. Analysis of the results determines the ability to classify rice by age and the general robustness of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%