Research on Fish Image Classification Based on Transfer Learning and Convolutional Neural Network Model

Ma, Yi; Zhang, Pengfei; Tang, Yunhan

doi:10.1109/fskd.2018.8686892

Cited by 14 publications

(5 citation statements)

References 5 publications

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“…-Sensitivity is defined as the ability to measure the proportion of positives that are correctly identified. It can be estimated as (12):…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Recent developments in machine learning algorithms are among the most widely used for FC [8]. Generally, fish identification can be categorized into two groups as follows [9]: i) classification through internal identification [10], [11] in which attributes such as the primary structural framework and length could be extracted, then a fish expert database was established, and the fish was identified with the help of an algorithm [12] and ii) classification through the identification of the exterior part of the fish [13], [14]. An increasing number of studies have found that the effective and basic utilized strategy is to take pictures of fish by photo capture devices.…”

Section: Introductionmentioning

confidence: 99%

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Deep convolutional neural network-based system for fish classification

Mehmood

Abugabah

Almekhlafi³

et al. 2022

IJECE

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<p>In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), adaptive max pooling (Adamax), Root mean square propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classification, among others.</p>

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“…-Sensitivity is defined as the ability to measure the proportion of positives that are correctly identified. It can be estimated as (12):…”

Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network-based system for fish classification

Mehmood

Abugabah

Almekhlafi³

et al. 2022

IJECE

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“…The neural network model, named ResNet50, that was trained using the deep residual learning method won the ILSVRC 2015 and outperformed the winner from the previous year's ILSVR competition. Since then, the ResNet50 pre-trained model has been applied to many classification tasks, and, according to a study in [34], RestNet-50 performs better in achieving a higher accuracy and lower error rate than the performances by AlexNet, GoogleNet, and VGG16 (Figure 4).…”

Section: Resnet50 Pre-trained Modelmentioning

confidence: 99%

Wall Crack Multiclass Classification: Expertise-Based Dataset Construction and Learning Algorithms Performance Comparison

et al. 2022

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Wall crack detection is one of the primary tasks in determining the structural integrity of a building for both restorative and preventive attempts. Machine learning techniques, such as deep learning (DL) with computer vision capabilities, have gradually become more prevalent as they can provide expert assessments with an acceptable performance when the crack detection involves a considerable number of structures. Despite such a prospective application, classification on different types of wall cracks is relatively less common, possibly due to the absence of the professional-standard-to-dataset translation. In this work, we utilised a complete pipeline, starting from novel dataset construction, ground truth formulation based on civil engineering standards, and training and testing steps. Our work focused on multi-class classification with regard to the binary classification (i.e., determining only two categories) used in previous studies. We implemented transfer learning based on VGG16 and RestNET50 for feature extraction, combined them with an ANN and kNN for the classifier, and compared their prediction performances. Our results indicate that the developed models can distinguish images that contain wall cracks into three categories of features based on the degree of damage: light, medium, and severe. Furthermore, since greyscale images offer more precise readings and predictions, the use of augmentation in dataset generation is critical. Although ResNet50 is the most stable network in terms of accuracy, it performs better when paired with kNN.

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“…Andayani et al [14] used a combination of geometric invariant moment, gray-Level co-occurrence matrix (GLCM), and hue saturation value (HSV) feature extraction methods to extract fish images features and for fish species classification purposes, they used probabilistic neural network (PNN) method utilized to properly classify fish species and achieved 89.65% accuracy rate. Others utilize convolutional neural networks (CNN) using deep learning for fish classification [15]- [17]. Sun et al [18] proposed DNN and super-resolution approach methods for explicitly learn the discriminative features from low-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Robust features extraction from shape signature for fish images classification

Ahmed

Hussein

Gali

2023

IJEECS

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Recently, the process of fish species classification has become one of the most challenging problems addressed by researchers. In this work, a robust scheme to classify fish images based on robust feature extraction from shape signatures is proposed. First, the image contour is fitted using one of the common approaches named radial basis function neural network (RBFNN) fitting to obtain image centroid. Afterward, prominent features from the shape signature are extracted. These features are representative of fish shapes because they can distinguish the characteristics of each class as well as being relatively robust to scale and rotation changes. Finally, for the classification process purpose, RBFNN is used again for image classification against one of the most commonly used classification techniques called support vector machine (SVM). The proposed paradigm has been applied to a standard fish dataset acquired from a live video dataset grouped into twenty-three clusters representing specific fish species. The resulting accuracy based on SVM and RBFNN was 90.41% and 98.04%, respectively.

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Research on Fish Image Classification Based on Transfer Learning and Convolutional Neural Network Model

Cited by 14 publications

References 5 publications

Deep convolutional neural network-based system for fish classification

Deep convolutional neural network-based system for fish classification

Wall Crack Multiclass Classification: Expertise-Based Dataset Construction and Learning Algorithms Performance Comparison

Robust features extraction from shape signature for fish images classification

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