Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification

Shabbir, Sidrah; Oliva, Diego; Mazzara, Manuel; Distefano, Salvatore

doi:10.1016/j.ijleo.2019.163712

Cited by 40 publications

(22 citation statements)

References 26 publications

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“…Clusters are used to annotate the healthy and unhealthy (spoiled) samples for classification [21], [44], [45]. Consequently, the changed pixels have been labeled as spoiled while the unchanged ones as healthy.…”

Section: Figure 5: Principal Component Analysismentioning

confidence: 99%

“…In SI, the system that acquires tens or fewer channels/bands is termed as Multispectral Imaging System (MSI). On the contrary, the system that has ten to a few hundred bands referred to as the Hyperspectral Imaging (HSI) [18], [21]. HSI captures data in the form of a hypercube by storing it in two spatial coordinates (x and y) and one spectral dimension (λ) [22].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

et al. 2020

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The shelf life of bakery products highly depends on the environment and it may get spoiled earlier than its expiry which results in food-borne diseases and may affect human health or may get wasted beforehand. The traditional spoilage detection methods are time-consuming and destructive in nature due to the time taken to get microbiological results. To the best of the author's knowledge, this work presents a novel method to automatically predict the microbial spoilage and detect its spatial location in baked items using Hyperspectral Imaging (HSI) range from 395 − 1000 nm. A spectral preserve fusion technique has been proposed to spatially enhance the HSI images while preserving the spectral information. Furthermore, to automatically detect the spoilage, Principal Component Analysis (PCA) followed by K-means and SVM has been used. The proposed approach can detect the spoilage almost 24 hours before it started appearing or visible to a naked eye with 98.13% accuracy on test data. Furthermore, the trained model has been validated through external dataset and detected the spoilage almost a day before it started appearing visually.

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Section: Figure 5: Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

et al. 2020

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“…Qi and Manasa et al [4,15] provided an idea on how to optimize the feature redundancy process and improve classification efficiency and accuracy. Also, Extreme learning machine (ELM) is often used in image classification research because of its advantages in learning rate and generalization ability [16]. Su et al [17] established an automatic defect identification system for solder joints by extracting texture features of welding defects.…”

Section: Introductionmentioning

confidence: 99%

A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects

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Welding quality directly affects the welding structure's service performance and life. Hence, the effective monitoring welding defects is essential to ensure the quality of the weld structure. Owing to the non-uniformity of the shape, position and size of welding defects, it is a complicated task to analyze and evaluate the acquired welding defects images manually. Fortunately, deep learning has been successfully applied to image analysis and target recognition. However, the use of deep learning to identify welding defects is time-consuming and less accurate due to the lack of adequate training data samples, which easily cause redundancy into the classifier. In this situation, we proposed a new transfer learning model based on MobileNet as a welding defect feature extractor. By using the ImageNet dataset (non-welding defect data) to pre-train a MobileNet model, migrate the MobileNet model to the welding defects classification field. This article suggested a new TL-MobileNet structure by adding a new Full Connection layer (FC-128) and a Softmax classifier into a traditional model called MobileNet. The entire training process of TL-MobileNet model has been successfully optimized by the DropBlock technology and Global average pooling (GAP) method. They can effectively accelerate the convergence rate and improve the classification network generalization. By testing the proposed TL-MobileNet on the welding defects dataset, it turned out our model prediction accuracy has arrived at 97.69%. The experimental results show that in several aspects, TL-MobileNet have better performance than other transfer learning models and traditional neural network methods.

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“…For traditional image classification and analysis, the spatial correlation of neighboring pixels contains important information [11]. Convolutional Neural Network (CNN) is designed to extract such information i.e., end to end feature extraction and classification through weights and pooling [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

et al. 2020

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Malaria refers to a contagious mosquito-borne disease caused by parasite genus plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a person, the parasite multiplies in the host's liver and start destroying the red-cells. The disease is examined visually under the microscope for infected red-cells. This diagnosis depends upon the expertise and experience of pathologists and reports may vary in different laboratories doing a manual examination. Another way around, many machine learning techniques have been applied for spontaneous detection of blood smears. However, feature engineering is a challenging task that requires expertise to adjust positional and morphological features. Therefore, this study proposes a novel Stacked Convolutional Neural Network architecture that improves the automatic detection of malaria without considering the hand-crafted features. The 5-fold cross-validation process on 27, 558 cell images with equal instances of parasitized and uninfected cells on a publicly available dataset from the National Institute of health, the accuracy of our proposed model is 99.98%. Furthermore, the statistical results revealed that the proposed model is superior to the state-of-the-art models with 100% precision, 99.9% recall, and 99% f1-measure. INDEX TERMS Convolutional neural network (CNN), Malaria, blood smear images, deep learning, diagnostic approach.

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Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification

Cited by 40 publications

References 26 publications

Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

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