Fish Classification in Context of Noisy Images

Ali-Gombe, Adamu; Elyan, Eyad; Jayne, Chrisina

doi:10.1007/978-3-319-65172-9_19

Cited by 27 publications

(21 citation statements)

References 12 publications

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“…The activation function selected was rectified linear unit (ReLU), which is a commonly used function with CNNs. The four models have fully connected layers at the end, with an activation layer bearing a softmax function, which is a categorical classifier widely used in DL architectures [68]. For training, two different optimizers were selected.…”

Section: Methodsmentioning

confidence: 99%

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Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

López-Vázquez¹,

López-Guede

Marini

et al. 2020

Sensors

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An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

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Section: Methodsmentioning

confidence: 99%

“…Convolutional neural networks (CNNs or ConvNets) have shown good accuracy results solving underwater classification problems [68][69][70]. Deep neural networks (DNNs) have also been used successfully in this field [71].…”

Section: An Array Containing the Haralick Features Of The Imagementioning

confidence: 99%

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

López-Vázquez¹,

López-Guede

Marini

et al. 2020

Sensors

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show abstract

“…Artificial variations are useful in minimizing any bias in data collection and class-imbalanced problem. For instance, in image domain, augmentation techniques used could range from simple image flips [3], random crops [4], noise [3] distortions to more advanced techniques like PCA colour augmentation [4] and image-pairing [5]. Data augmentation technique can be a source of more training data [6] or a regularizer [5] thereby improving generalization.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

MFC-GAN: Class-imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network

2019

Self Cite

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Class-imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class-instances. Data Augmentation is a common approach that aims at rebalancing a dataset by injecting more data samples of the minority class instances. In this paper, a new data augmentation approach is proposed using a Generative Adversarial Networks (GAN) to handle the class imbalance problem. Unlike common GAN models, which use a single fake class, the proposed method uses multiple fake classes to ensure a fine-grained generation and classification of the minority class instances. Moreover, the proposed GAN model is conditioned to generate minority class instances aiming at rebalancing the dataset. Extensive experiments were carried out using public datasets, where synthetic samples generated using our model were added to the imbalanced dataset, followed by performing classification using Convolutional Neural Network. Experiment results show that our model can generate diverse minority class instances, even in extreme cases where the number of minority class instances is relatively low. Additionally, superior performance of our model over other common augmentation and oversampling methods was achieved in terms of classification accuracy and quality of the generated samples.

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“…In addition to the inherent classical machine vision problems such as light, scale and pose variations, these drawings use equipment symbols with different standards for different industries 4 . Therefore, compiling a well-defined and clearly labelled dataset that can be used for symbol classification is a complicated task.…”

Section: Symbolsmentioning

confidence: 99%

“…Moreover, transfer learning, which attempts to reproduce the success of a model on a similar task, has been considered to address this issue [125]. Recently, Ali-Gombe et al [4] presented a comparative study of data augmentation and transfer learning on the context of fish classification, finding that manual annotation of data was a key requirement to increase accuracy rates for these options.…”

Section: New Trends In Engineering Drawing Digitisationmentioning

confidence: 99%

New trends on digitisation of complex engineering drawings

Moreno‐García

Elyan

Jayne

2018

Neural Comput & Applic

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Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions.

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Fish Classification in Context of Noisy Images

Cited by 27 publications

References 12 publications

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

MFC-GAN: Class-imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network

New trends on digitisation of complex engineering drawings

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