Cross-Modal Image Clustering via Canonical Correlation Analysis


  • Cheng Jin Fudan Univeristy
  • Wenhui Mao Fudan Univeristy
  • Ruiqi Zhang Fudan Univeristy
  • Yuejie Zhang Fudan University
  • Xiangyang Xue Fudan University



A new algorithm via Canonical Correlation Analysis (CCA) is developed in this paper to support more effective cross-modal image clustering for large-scale annotated image collections. It can be treated as a bi-media multimodal mapping problem and modeled as a correlation distribution over multimodal feature representations. It integrates the multimodal feature generation with the Locality Linear Coding (LLC) and co-occurrence association network, multimodal feature fusion with CCA, and accelerated hierarchical k-means clustering, which aims to characterize the correlations between the inter-related visual features in images and semantic features in captions, and measure their association degree more precisely. Very positive results were obtained in our experiments using a large quantity of public data.




How to Cite

Jin, C., Mao, W., Zhang, R., Zhang, Y., & Xue, X. (2015). Cross-Modal Image Clustering via Canonical Correlation Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).