Improving Leaf Classification Rate via Background Removal and ROI Extraction

Wu, Yen‐Ju; Tsai, Chun-Ming; Shih, Frank Y.

doi:10.18178/joig.4.2.93-98

Cited by 34 publications

(7 citation statements)

References 10 publications

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“…They have used background removal and the ROI extraction methods as the novel implementation and achieved 92.13% accuracy. 8 Moreover, an object recognition technology has been introduced using python and MNIST dataset modification by Karayaneva and Hintea. They have used five machine learning algorithms, including neural networks and achieved 87%-98% accuracy on object recognition.…”

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)

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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.

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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)

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“…Image processing is a component of the system's lower-level image analysis or computer vision functionality. With the aid of deep learning (DL), fuzzy systems, and improved feature extraction procedures, etc., image processing has made significant advances in recent years [1][2][3][4]. Currently, transformer models are gaining popularity in computer vision tasks.…”

Section: Introductionmentioning

confidence: 99%

How certain are tansformers in image classification: uncertainty analysis with Monte Carlo dropout

Islam

Zabeen

Islam

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Researchers have been inspired to implement transformer models in solving machine vision problems after their tremendous success with natural language tasks. Using the straightforward architecture and swift performance of transformers, a variety of computer vision problems can be solved with more ease and effectiveness. However, a comparative evaluation of their uncertainty in prediction has not been done yet. As we know, real world applications require a measure of uncertainty to produce accurate predictions, which allows researchers to handle uncertain inputs and special cases, in order to successfully prevent overfitting. Our study approaches the unexplored issue of uncertainty estimation among three popular and effective transformer models employed in computer vision, such as Vision Transformers (ViT), Swin Transformers (SWT), and Compact Convolutional Transformers (CCT). We conduct a comparative experiment to determine which particular architecture is the most reliable in image classification. We use dropouts at the inference phase in order to measure the uncertainty of these transformer models. This approach, commonly known as Monte Carlo Dropout (MCD), works well as a low-complexity estimation to compute uncertainty. The MCD-based CCT model is the least uncertain architecture in this classification task. Our proposed MCD-infused CCT model also yields the best results with 78.4% accuracy, while the SWT model with embedded MCD exhibits Pmaximum performance gain where the accuracy increased by almost 3% with the final result being 71.4%.

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“…Convolutional neural networks (CNN) have been widely applied in many computer-vision tasks, such as image classification [1][2][3] , image super-resolution 4 , object detection 5,6 . However, the size of many advanced CNN models is too large for most mobile and embedded devices, which hindering the practical application of CNN.…”

Section: Introductionmentioning

confidence: 99%

Compressing CNN by alternating constraint optimization framework

Liu

Liu²,

Shi³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Tensor decomposition has been extensively studied for convolutional neural networks (CNN) model compression. However, the direct decomposition of an uncompressed model into low-rank form causes unavoidable approximation error due to the lack of low-rank property of a pre-trained model. In this manuscript, a CNN model compression method using alternating constraint optimization framework (ACOF) is proposed. Firstly, ACOF formulates tensor decomposition-based model compression as a constraint optimization problem with low tensor rank constraints. This optimization problem is then solved systematically in an iterative manner using alternating direction method of multipliers (ADMM). During the alternating process, the uncompressed model gradually exhibits low-rank tensor property, and then the approximation error in low-rank tensor decomposition can be negligible. Finally, a high-performance CNN compression network can be effectively obtained by SGD-based fine-tuning. Extensive experimental results on image classification show that ACOF produces the optimal compressed model with high performance and low computational complexity. Notably, ACOF compresses Resnet56 to 28% without accuracy drop, and the compressed model have 1.14% higher accuracy than learning-compression (LC) method.

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Improving Leaf Classification Rate via Background Removal and ROI Extraction

Cited by 34 publications

References 10 publications

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

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

How certain are tansformers in image classification: uncertainty analysis with Monte Carlo dropout

Compressing CNN by alternating constraint optimization framework

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