Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion

TL;DR

This paper introduces a novel approach for tag completion and refinement in image retrieval by combining subspace clustering with low-rank matrix completion, effectively handling missing and noisy tags.

Contribution

It formulates tag completion as a subspace clustering problem and employs Low Rank Representation along with matrix completion for improved tag accuracy.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively handles missing and noisy tags

Improves accuracy of image annotation

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.