Fish Species Classification Using Graph Embedding Discriminant Analysis

Hasija, Snigdhaa; Buragohain, Manas Jyoti; Indu, S.

doi:10.1109/cmvit.2017.23

Cited by 29 publications

(20 citation statements)

References 14 publications

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“…Only recently has the use of raw image features in its intrinsic highdimensionality become more feasible, likely because of advances in computational capabilities. For example, (Hasija et al, 2017) employed graph-embedding discriminant analysis, which reduces the image set matching problem to a point-to-point classification problem.…”

Section: Damaged Specimenmentioning

confidence: 99%

Hierarchy-guided Neural Networks for Species Classification

Elhamod

Diamond²,

Maga

et al. 2021

Preprint

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Fish species classification is an important task that is the foundation of many industrial, commercial, ecological, and scientific applications involving the study of fish distributions, dynamics, and evolution.While conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes.In this work, we propose infusing phylogenetic information into the model’s training to guide its structure and relationships among the extracted features. In our extensive experimental analyses, the proposed model, named Hierarchy-Guided Neural Network (HGNN), outperforms conventional ConvNet models in terms of classification accuracy under scarce training data conditions.We also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied.

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Section: Damaged Specimenmentioning

confidence: 99%

Hierarchy-guided Neural Networks for Species Classification

Elhamod

Diamond²,

Maga

et al. 2021

Preprint

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“…Classifying fish can be valuable for various purposes one of which is the identification of different fish species. Classifying fish accurately are beneficial for the study of fish diversity [1]. Aside from this, the grouping of fishes is additionally valuable for learning the deportment and interspecies cooperation of fishes in a typical natural condition [2].…”

Section: Datamentioning

confidence: 99%

Fish-Pak: Fish species dataset from Pakistan for visual features based classification

et al. 2019

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Fishes are most diverse group of vertebrates with more than 33000 species. These are identified based on several visual characters including their shape, color and head. It is difficult for the common people to directly identify the fish species found in the market. Classifying fish species from images based on visual characteristics using computer vision and machine learning techniques is an interesting problem for the researchers. However, the classifier's performance depends upon quality of image dataset on which it has been trained. An imagery dataset is needed to examine the classification and recognition algorithms. This article exhibits Fish-Pak: an image dataset of 6 different fish species, captured by a single camera from different pools located nearby the Head Qadirabad, Chenab River in Punjab, Pakistan. The dataset Fish-Pak are quite useful to compare various factors of classifiers such as learning rate, momentum and their impact on the overall performance. Convolutional Neural Network (CNN) is one of the most widely used architectures for image classification based on visual features. Six data classes i.e. Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla (Thala), with a different number of images, have been included in the dataset. Fish species are captured by one camera to ensure the fair environment to all data. Fish-Pak is hosted by the Zoology Lab under the mutual affiliation of the Department of Computer Science and the Department of Zoology, University of Gujrat, Gujrat, Pakistan.

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“…Fish species classification survey also needs to be standardized, Caldwell ZR, et al, used the visual census technology, through the assessment for coral reef fish populations, to implement the standardization of fish population survey method [77]. S Hasija, et al, overcome the difficulties of underwater image classification and computer calibration, this paper proposed a matching method based on improved image sets, the method used a graphical embedded discriminant analysis method, it can realize precise classification of the fish species [78].…”

Section: Classification Of Fish Species Based On Computer Visionmentioning

confidence: 99%