Abstract
This paper proposes an image feature classification method that applies a distance learning neural network to image feature vectors extracted from an autoencoder. There is active research on similar image retrieval methods using image feature vectors extracted from neural networks. If the image classification performance is not sufficient, it is possible to further improve it by applying a distance learning neural network to convert it into an image feature vector for obtaining appropriate ranking results. In the proposed method, by constructing a model that connects an autoencoder and a distance learning neural network, the reusability of image features extracted from the autoencoder is maintained. In addition, it allows the model to flexibly be combine the autoencoder and distance learning neural network for the model construction. In the experiment, we evaluate the image classification accuracy using an aerial photo dataset provided by the Geospatial Information Authority of Japan and confirm the feasibility of the proposed method.
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Acknowledgments
This research was supported by the Collaboration Research Program of IDEAS, Chubu University IDEAS202303, and by JSPS Grant-in-Aid for Scientific Research 23K11120.
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Seo, Y., Kanza, R.A., Watanabe, N., Li, K.F., Takano, K. (2024). A Classification Method of Image Feature Using Neural Metric Learning for Natural Environment Video. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-031-57942-4_40
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DOI: https://doi.org/10.1007/978-3-031-57942-4_40
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