New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique

Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Pergiwati, D.; Sari, Yuslena; Andono, Pulung Nurtantio; Soeleman, Moch Arief

doi:10.22266/ijies2020.0831.26

Cited by 9 publications

(5 citation statements)

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“…Then from the data, the conversion of RGB image data is converted into a grayscale image. Feature extraction using gray level co-occurrence matrix (GLCM) is performed to obtain features in the form of the angular second moment (ASM), contrast, different inverse moment (IDM), entropy, and correlation [12]. GLCM feature is classified using the support vector machine (SVM) method.…”

Section: Proposed Methodsmentioning

confidence: 99%

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

2021

IJIES

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The increasing ownership of four-wheeled vehicles creates a new problem: difficulty finding available parking spaces in significant places. Issues citizens often experience these in the city centre who use their cars for transportation. This causes the detection of parking lots to attract the attention of researchers. Unfortunately, this system fails when the vehicle occupies more than one place or when the parking lot has a different parking space. In this study, an automation method is proposed that utilises a combination of gray level co-occurrence matrix (GLCM) and support vector machine (SVM) methods with genetic algorithm (GA) optimisation techniques or called SVMGA. The results showed that the SVMGA could provide an accuracy performance of 96.99% for training data and 94.36% for test data. The results of this study are expected to help the queue of people to find a parking space and reduce the density of lines in the parking lot.

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Section: Proposed Methodsmentioning

confidence: 99%

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

2021

IJIES

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“…The assessment is performed by dividing the total quantity of test data within the data set by the number of recognized test data. Using equation (7), accuracy is computed.…”

Section: E Evaluationmentioning

confidence: 99%

“…Since LBP is insensitive to photometric variations of the same object, it is regarded as quite reasonable and resistant to numerous lighting disturbances in the image [6]. The GLCM method was selected because its quantization step aids in reducing noise, and statistical functions are used to determine the image's features [7].…”

Section: Introductionmentioning

confidence: 99%

Mixture Feature Extraction Based on Local Binary Pattern and Grey-Level Co-Occurrence Matrix Techniques for Mouth Expression Recognition

Pramunendar

Prabowo²,

Sari³

2022

JTekInfULM

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Some academics struggle to recognize facial emotions based on pattern recognition. In general, this recognition utilizes all facial features. However, this study was limited to identifying facial emotions in a single facial region. In this study, lips, one of the facial features that can reveal a person's expression, are utilized. Using a combination of local binary pattern feature extraction (LBP) and grey level co-occurrence matrix (GLCM) methods and a multiclass support vector machine classification approach for feature extraction in facial images. The concept begins with image segmentation to create an image of a mouth. Experiments were also conducted for various tests, and the outcomes of these experiments revealed a recognition performance of up to 95%. This result was obtained through experiments in which 10% to 40% of the data were evaluated. These findings are beneficial and can be applied to expression recognition in online learning media to monitor the audience's condition directly.

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“…The following research is the classification of Katsuwonus Pelamis (Skipjack tuna or Skipjack), Euthynnus Affinis (Tongkol) and Coryphaena Hippurus (Mahi-mahi) using transfer learning and Matlab applications, obtaining an accuracy of 99.63% [11]. The next research will classify fish species using a combination of contrast limited adaptive histogram equalization (CLAHE) with adaptive features threshold by fuzzy c-means [12]. In addition, research related to fish detection and species classification uses deep learning and obtains an accuracy of 91.2% [13].…”

Section: Literature Viewmentioning

confidence: 99%

Classification of Nile Tilapia’s Freshness Based on Eyes and Gills Using Support Vector Machine

Syarwani¹,

Nugraha²,

Dwiyansaputra³

et al. 2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and

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Fish is one of the foodstuffs that contain high protein and essential amino acids the body needs. Nile Tilapia is a fish that the people of Indonesia widely consume. The high nutritional content of tilapia and affordable prices make this fish popular with the public. The difference between fresh and unfresh tilapia can be assessed from organoleptic tests, including gill color, texture, and smell. Consumers can check by looking at the condition of tilapia based on its distinguishing physical characteristics such as eyes, gills, flesh texture, skin, and fish mucus. However, not everyone knows and understands these typical characteristics. Therefore, we need a system that can classify the freshness level of tilapia. In this study, the freshness level of tilapia will be classified based on the color and texture features of the eyes and gills using the Support Vector Machine. The GLCM approach is used to extract texture features, whereas the HSV method is utilized to extract color features. The total number of photos used in this investigation was 840, which were separated into training and testing data. With an image size of 256 × 256 pixels, the combined feature of HSV + GLCM achieves the highest accuracy of 94.28%.

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New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique

Cited by 9 publications

References 0 publications

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

Mixture Feature Extraction Based on Local Binary Pattern and Grey-Level Co-Occurrence Matrix Techniques for Mouth Expression Recognition

Classification of Nile Tilapia’s Freshness Based on Eyes and Gills Using Support Vector Machine

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