Going Deeper on BioImages Classification: A Plant Leaf Dataset Case Study

Alves, Daniel H. A.; Galonetti, Luís F.; Oliveira, Claiton de; Bugatti, Pedro Henrique; Saito, Priscila T. M.

doi:10.1007/978-3-319-75193-1_5

Cited by 2 publications

(1 citation statement)

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“…The Pl@antLeaves II [48] dataset is a subset of the ImageCLEF2012 [49] dataset, which contains different types of leaves from trees of the Mediterranean region of France. The subset considered in our experiments is composed by 3, 655 scan-like samples from 53 classes (species).…”

Section: Plos Onementioning

confidence: 99%

Active semi-supervised learning for biological data classification

2020

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Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semisupervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones.

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Section: Plos Onementioning

confidence: 99%

Active semi-supervised learning for biological data classification

2020

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An Improved Approach for Real-Time Taillight Intention Detection by Intelligent Vehicles

Tong

Chen

et al. 2022

Machines

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Vehicle taillight intention detection is an important application for perception and decision making by intelligent vehicles. However, effectively improving detection precision with sufficient real-time performance is a critical issue in practical applications. In this study, a vision-based improved lightweight approach focusing on small object detection with a multi-scale strategy is proposed to achieve application-oriented real-time vehicle taillight intention detection. The proposed real-time detection model is designed based on YOLOv4-tiny, and a spatial pyramid pooling fast (SPPF) module is employed to enrich the output layer features. An additional detection scale is added to expand the receptive field corresponding to small objects. Meanwhile, a path aggregation network (PANet) is used to improve the feature resolution of small objects by constructing a feature pyramid with connections between feature layers. An expanded dataset based on the BDD100K dataset is established to verify the performance of the proposed method. Experimental results on the expanded dataset reveal that the proposed method can increase the average precision (AP) of vehicle, brake, left-turn, and right-turn signals by 1.81, 15.16, 40.04, and 41.53%, respectively. The mean average precision (mAP) can be improved by 24.63% (from 62.20% to 86.83%) at over 70 frames per second (FPS), proving that the proposed method can effectively improve detection precision with good real-time performance.

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Going Deeper on BioImages Classification: A Plant Leaf Dataset Case Study

Cited by 2 publications

References 20 publications

Active semi-supervised learning for biological data classification

Active semi-supervised learning for biological data classification

An Improved Approach for Real-Time Taillight Intention Detection by Intelligent Vehicles

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