Image Tag Refinement by Regularized Latent Dirichlet Allocation

TL;DR

This paper introduces a novel graphical model called regularized Latent Dirichlet Allocation (rLDA) for refining image tags by jointly estimating tag relevance and similarity, leveraging visual and tag statistics to improve image search.

Contribution

The paper presents a new topic modeling approach that iteratively refines image tags by exploring multi-wise tag relationships and integrating visual affinities, enhancing tag relevance accuracy.

Findings

Improved tag ranking performance.

Enhanced image retrieval accuracy.

Effective exploration of tag relationships.

Abstract

Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many manually-assigned tags are irrelevant to image content and hence are not reliable for applications. A lot of recent efforts have been conducted to refine image tags. In this paper, we propose to do tag refinement from the angle of topic modeling and present a novel graphical model, regularized Latent Dirichlet Allocation (rLDA). In the proposed approach, tag similarity and tag relevance are jointly estimated in an iterative manner, so that they can benefit from each other, and the multi-wise relationships among tags are explored. Moreover, both the statistics of tags and visual affinities of images in the corpus are explored to help topic modeling. We also analyze the superiority of our approach from the deep structure perspective. The experiments on tag ranking and image retrieval demonstrate the advantages of the proposed method.