2017/11/21 by Qianqian Wang, Wang, Qianqian, Xiaowei Zhou +3 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Multimodal Machine Learning Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.1711.07641
CVPR 2018
openalex publication_date 2017/11/21 · arxiv created 2018/05/01 · arxiv updated 2018/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method for reconstructing object-class models and discovering object-class landmarks from images without using any annotation.