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Chang D. Yoo
Researcher at KAIST
Publications - 204
Citations - 4439
Chang D. Yoo is an academic researcher from KAIST. The author has contributed to research in topics: Computer science & Digital watermarking. The author has an hindex of 27, co-authored 188 publications receiving 3357 citations. Previous affiliations of Chang D. Yoo include Microsoft & KT Corporation.
Papers
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Journal ArticleDOI
Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform
TL;DR: The experimental results show that the proposed scheme achieves higher embedding capacity while maintaining distortion at a lower level than the existing reversible watermarking schemes.
Proceedings ArticleDOI
Edge-Labeling Graph Neural Network for Few-Shot Learning
TL;DR: A novel edge-labeling graph neural network (EGNN) is proposed, which adapts a deep neural network on the edge- labeling graph, for few-shot learning and significantly improves the performances over the existing GNNs.
Posted Content
Edge-labeling Graph Neural Network for Few-shot Learning
TL;DR: In this article, a novel edge-labeling graph neural network (EGNN) is proposed, which adapts a deep neural network on the edge label graph for few-shot learning, enabling the evolution of an explicit clustering by iteratively updating the edge labels with direct exploitation of both intra-cluster similarity and the inter-clusters dissimilarity.
Journal ArticleDOI
A robust image fingerprinting system using the Radon transform
TL;DR: A new approach for image fingerprinting using the Radon transform to make the fingerprint robust against affine transformations, and addresses other issues such as pairwise independence, database search efficiency and key dependence of the proposed method.
Journal ArticleDOI
Robust Video Fingerprinting for Content-Based Video Identification
Sun-Il Lee,Chang D. Yoo +1 more
TL;DR: A novel video fingerprinting method based on the centroid of gradient orientations is proposed, and the experimental results show that the proposed fingerprint outperforms the considered features in the context ofVideo fingerprinting.