Perceptually Aligned Color Composition Similarity Using Graph Convolutional Networks

ABSTRACT

This paper presents a perceptually aligned framework for color‐composition‐based image similarity by representing images as graphs and learning their structures with graph convolutional networks (GCNs). Images are first segmented into homogeneous color regions, which are converted into nodes characterized by CIELAB color features and region statistics. Spatial adjacency among regions is encoded as edges, enabling the model to capture both global color distribution and localized relationships that are difficult to represent in conventional RGB‐based convolutional neural network (CNN) approaches. The proposed method processes each graph through a shared GCN‐based feature extraction network, followed by a similarity prediction network that estimates the similarity between images. Experimental results show that the model achieves higher correspondence with perceptual similarity judgments than the existing CNN‐based method. The graph representation also provides improved stability under geometric transformations such as rotations and reflections, and facilitates the preservation of subtle yet perceptually salient accent colors. These findings demonstrate the effectiveness of integrating CIELAB color features with graph‐based modeling for holistic analysis of color composition. The proposed framework offers a promising direction for applications such as content‐based image retrieval, visual design support, and color scheme analysis, contributing to more perceptually meaningful assessments of image similarity.