Learning Semantics for Image Annotation

TL;DR

This paper introduces a high-level semantic approach for image annotation by transforming keywords into image themes, using latent Dirichlet allocation and ConceptNet to improve annotation coherence and retrieval performance.

Contribution

The paper proposes a novel high-level semantics space for image annotation, integrating latent Dirichlet allocation and ConceptNet to enhance annotation quality and coherence.

Findings

Improved precision and recall over keyword-based systems.

Enhanced annotation coherence using image themes.

Effective use of ConceptNet for semantic augmentation.

Abstract

Image search and retrieval engines rely heavily on textual annotation in order to match word queries to a set of candidate images. A system that can automatically annotate images with meaningful text can be highly beneficial for such engines. Currently, the approaches to develop such systems try to establish relationships between keywords and visual features of images. In this paper, We make three main contributions to this area: (i) We transform this problem from the low-level keyword space to the high-level semantics space that we refer to as the "{\em image theme}", (ii) Instead of treating each possible keyword independently, we use latent Dirichlet allocation to learn image themes from the associated texts in a training phase. Images are then annotated with image themes rather than keywords, using a modified continuous relevance model, which takes into account the spatial coherence and the visual continuity among images of common theme. (iii) To achieve more coherent annotations among images of common theme, we have integrated ConceptNet in learning the semantics of images, and hence augment image descriptions beyond annotations provided by humans. Images are thus further annotated by a few most significant words of the prominent image theme. Our extensive experiments show that a coherent theme-based image annotation using high-level semantics results in improved precision and recall as compared with equivalent classical keyword annotation systems.