On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Loddo, Andrea; Ruberto, Cecilia Di

doi:10.3390/jimaging7090171

Cited by 14 publications

(5 citation statements)

References 39 publications

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“…No image construction was required, and no complex neural networks needed to be trained. In the literature, several works have also shown that handcrafted features can achieve comparable performance to deep learning approaches with the former having the merit of reduced computational complexity which could be attractive in real-time applications [ 106 , 107 , 108 ]. Another advantage of the presented method is that unlike in other literature where all the frequency bands or the raw EEG signal were considered, only the alpha band was used for feature extraction.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abdel-Hamid

2023

Sensors

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Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.

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Section: Discussionmentioning

confidence: 99%

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abdel-Hamid

2023

Sensors

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“…Rahman et al [30] also exploited TL strategies using both natural and medical images and performed an extensive test of some off-the-shelf CNNs to realise a binary classification. Some other techniques not explored in this work are based on the combination of CNNextracted features and handcrafted ones [31][32][33] or the direct use of object detectors [34]. For example, Kudisthalert et al [33] proposed a malaria parasite detection system, based on the combination of handcrafted and deep features, extracted from pretrained AlexNet.…”

Section: Related Workmentioning

confidence: 99%

“…Some other techniques not explored in this work are based on the combination of CNN-extracted features and handcrafted ones [ 31 , 32 , 33 ] or the direct use of object detectors [ 34 ]. For example, Kudisthalert et al [ 33 ] proposed a malaria parasite detection system, based on the combination of handcrafted and deep features, extracted from pretrained AlexNet.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

2022

Self Cite

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Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the P. falciparum stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments.

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“…Non-handcrafted features can be extracted using the compact binary descriptor (CBD), convolutional neural networks (CNN), or principal component analysis network (PCAN) [ 29 – 31 ]. In particular, CNN-based deep features are robust and efficient compared to the handcrafted features [ 18 , 29 , 32 ], due to their independence of prior knowledge and image extraction and applicability to any images. However, non-handcrafted features have the following limitations: (a) The interpretability of non-handcrafted features is low, which means that it is difficult to describe the learned features [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

Xie

Xia

et al. 2023

Plant Phenomics

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Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

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On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Cited by 14 publications

References 39 publications

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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