Integration GLCM and geometric feature extraction of region of interest for classifying tuna

Saputra, Wanvy Arifha; Herumurti, Darlis

doi:10.1109/icts.2016.7910276

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…The texture feature extraction process will be calculated based on the image's neighboring value from the center point. Among the number of neighbors 1,2,4,8,16,32,64,128. So the resulting value on texture feature extraction as much as 256 bins (space) for texture features.…”

Section: B Texture Extraction Featuresmentioning

confidence: 99%

“…The advantage of the proposed method is to provide better performance in accuracy and complexity than other operators. In the other image processing research is the detection of tuna based on texture and its shape using gray level co-occurrences matrix (GLCM) [2]. This method adds geometric feature extraction of the region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

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Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern

Kartika

Herumurti

Yuniarti

2018

IJPS

Self Cite

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A lot of methods are used to develop on image research. Image detection to relay back new information, widely used in various research field, such as health, agriculture or other field research. Various methods are used and developed to get better results. A combination of several methods is performed for testing as part of the research contribution. In this study will perform the combination results of the process color feature extraction with texture features. In color feature extraction using HSV color space method that gets 72 feature extraction and on texture feature extraction using local binary pattern that gets 256 feature extraction. The process of merging the two extracted results gets 328 new feature extractions. The result of combining color feature extraction and texture feature extraction is further classified. Results from image classification of butterflies get an accuracy score of 72%. The results obtained will be tested performance. The results obtained from performance testing get precision value, recall and f-measure respectively 76%, 72% and 74%.

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Section: B Texture Extraction Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern

Kartika

Herumurti

Yuniarti

2018

IJPS

Self Cite

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“…In terms of performance, the comparison of the performance of the Surf, Harris, Brisk, and Fask feature extraction methods shows that the Surf method is the best in classifying images [14]. Furthermore, from the integration of the GLCM feature extraction method and geometric feature extraction of a region of interest (ROI) for classifying tuna, it was found that the best classification accuracy was 86.76% obtained through the GLCM method [15]. In addition, the use of the HSV color feature extraction method and GLCM texture feature extraction to identify the type of woven fabric shows that the accuracy of the color and texture combination features is 91.67% [16].…”

Section: Introductionmentioning

confidence: 99%

Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Latumakulita

Astawa

Mairi

et al. 2022

JOIV : Int. J. Inform. Visualization

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Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

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“…Research in the field of the image is widely used in various areas, for example, facial recognition using ESLGS (Extended Symmetric Local Graph Structure) method is an improvement over the previous method of SLGS [1]. Face recognition accuracy using the ESLGS method obtained an accuracy of 84.24%, compared with the previous method of SLGS of 80.59%.…”

Section: Introductionmentioning

confidence: 99%

Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

Kartika¹

2018

GEOMATE

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ABSTRACT:Research in the field of information retrieval especially on image processing is proliferating. Various methods are developed to be able to detect images optimally and produce better accuracy. The process of image detection can use the dataset that exists around us. In this research, we use butterflies dataset, since the butterfly has unique colors, patterns, and diverse shapes. Therefore, we use local binary pattern method for texture feature extraction and region props for shape feature extraction. The results of each texture feature extraction and shape feature extraction will be a merging process. The results of the merging process get an accuracy of 66%. In addition, the system testing process with confusion matrix will produce 67.1% precision value, 66% recall and f-measure of 66.5%. The merging process of both methods shows the interplay of texture and shape extraction.

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Integration GLCM and geometric feature extraction of region of interest for classifying tuna

Cited by 8 publications

References 6 publications

Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern

Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern

Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

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