Generative Model Construction Based on Highly Rated Koi Images to Evaluate Koi Quality

Nishikigoi are highly valued ornamental fish whose evaluation affects their market price. However, the judging criteria of the exhibitions remain unclear. This study applies a generative artificial intelligence model to explore potential factors behind non-award-winning Kohaku Nishikigoi. An improved Variational Autoencoder (VAE) is developed based on the standard VAE as follows: introducing perceptual loss to enhance detail, adding mask loss to maintain body shape consistency, and replacing transposed convolutions with UpSampling layers to reduce artifacts. With the improved VAE, we propose a method to evaluate a non-award-winning Koi. Specifically, when the non-award-winning images are input into the model, differences between the input and output images become large to identify visual deficiencies of the inputs, since the improved VAE is designed to generate images that potentially win competitions. For experiments, synthetic non-award-winning Koi images were created by modifying award-winning ones. The synthesized non-award-winning images were input into the improved VAE and the generated images were obtained. Experimental results showed that shape consistency measured by Multi-layer Sliding Window was lower for award-winning images (0.110) than for non-award-winning images (0.141). Also, the average difference in color was smaller for award-winning Koi (4.75%) than for non-award-winning Koi (28.7%).