@article{Jin_Mao_Zhang_Zhang_Xue_2015, title={Cross-Modal Image Clustering via Canonical Correlation Analysis}, volume={29}, url={https://ojs.aaai.org/index.php/AAAI/article/view/9181}, DOI={10.1609/aaai.v29i1.9181}, abstractNote={ <p> 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. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jin, Cheng and Mao, Wenhui and Zhang, Ruiqi and Zhang, Yuejie and Xue, Xiangyang}, year={2015}, month={Feb.} }