Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3
Deep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding re...
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Ikatan Ahli Informatika Indonesia
2025-05-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6356 |
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| author | Brian Sabayu Imam Yuadi |
| author_facet | Brian Sabayu Imam Yuadi |
| author_sort | Brian Sabayu |
| collection | DOAJ |
| description | Deep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding realism, texture, and style. This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. The research employs Silhouette Graphs, Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to assess clustering and classification efficiency. Support Vector Machines (SVM) and Logistic Regression are utilized to compute the Area Under the Curve (AUC), Classification Accuracy (CA), and Mean Squared Error (MSE). The MDS plots and t-SNE data clearly demonstrate the capability of the three deep learning models to distinguish between the image categories. For game-captured images, VGG-16 and VGG-19 demonstrate quite outstanding performance with silhouette scores of 0.398 and 0.315, respectively. This study explores the enhancement of classification accuracy in logistic regression and support vector machines (SVM) through the refinement of decision boundaries for overlapping categories. Utilizing Inception V3, an artificial intelligence-generated image silhouette score of 0.244 was achieved, demonstrating proficiency in image classification. The research highlights the challenges posed by diverse datasets and the efficacy of deep learning models in the classification of red fox images. The findings suggest that integrating deep learning with machine learning classifiers, such as logistic regression and SVM, may improve classification accuracy. |
| format | Article |
| id | doaj-art-8eaa441b201446d88e2adf0e0003c699 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-8eaa441b201446d88e2adf0e0003c6992025-08-20T03:28:22ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-05-019343544310.29207/resti.v9i3.63566356Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3Brian Sabayu0Imam Yuadi1Universitas AirlanggaUniversitas AirlanggaDeep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding realism, texture, and style. This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. The research employs Silhouette Graphs, Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to assess clustering and classification efficiency. Support Vector Machines (SVM) and Logistic Regression are utilized to compute the Area Under the Curve (AUC), Classification Accuracy (CA), and Mean Squared Error (MSE). The MDS plots and t-SNE data clearly demonstrate the capability of the three deep learning models to distinguish between the image categories. For game-captured images, VGG-16 and VGG-19 demonstrate quite outstanding performance with silhouette scores of 0.398 and 0.315, respectively. This study explores the enhancement of classification accuracy in logistic regression and support vector machines (SVM) through the refinement of decision boundaries for overlapping categories. Utilizing Inception V3, an artificial intelligence-generated image silhouette score of 0.244 was achieved, demonstrating proficiency in image classification. The research highlights the challenges posed by diverse datasets and the efficacy of deep learning models in the classification of red fox images. The findings suggest that integrating deep learning with machine learning classifiers, such as logistic regression and SVM, may improve classification accuracy.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6356red fox imagesimage classificationdeep learning models |
| spellingShingle | Brian Sabayu Imam Yuadi Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) red fox images image classification deep learning models |
| title | Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 |
| title_full | Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 |
| title_fullStr | Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 |
| title_full_unstemmed | Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 |
| title_short | Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 |
| title_sort | classification of red foxes logistic regression and svm with vgg 16 vgg 19 and inception v3 |
| topic | red fox images image classification deep learning models |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6356 |
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