Year: 2015
Type: conference-paper
Abstract: Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, the TBIR applications still suffer from the deficient and inaccurate tags provided by users. Inspired by the subspace clustering methods, we formulate the tag completion problem in a subspace clustering model which assumes that images are sampled from subspaces, and complete the tags using the state-of-the-art Low Rank Representation (LRR) method. And we propose a matrix completion algorithm to further refine the tags. Our empirical results on multiple benchmark datasets for image annotation show that the proposed algorithm outperforms state-of-the-art approaches when handling missing and noisy tags.
Authors: Yuqing Hou, Zhouchen Lin
Institutions: Peking University, HTC (Taiwan), Shanghai Jiao Tong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2962884438