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[3D1-GS-2-04] A Classification Method for "kawaii" Images using Feature Vectors with Semantic Interpretation of Image Composition Content
Keywords:Kawaii, Image Classification, Feature Operation
Image classification using machine learning is well-researched, yet categorizing “kawaii” images from human sensibility remains challenging due to its subjective aspects.
Previous studies on “kawaii” images have achieved 70.2% accuracy by extracting color and shape features.
We proposes a classification method based on the constituent objects in “kawaii” images, which has not been used by previous research.
In our experiments, we created feature vectors that not only quantitatively but also semantically represent the objects in the image, and input them to a machine learning learning-based classifier for classification.
As a result, classification was possible with an accuracy of up to 71.9%.
Experiments were also conducted with different conditions for searching from within the image, and the features and results were discussed.
Previous studies on “kawaii” images have achieved 70.2% accuracy by extracting color and shape features.
We proposes a classification method based on the constituent objects in “kawaii” images, which has not been used by previous research.
In our experiments, we created feature vectors that not only quantitatively but also semantically represent the objects in the image, and input them to a machine learning learning-based classifier for classification.
As a result, classification was possible with an accuracy of up to 71.9%.
Experiments were also conducted with different conditions for searching from within the image, and the features and results were discussed.
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