Semantic clustering of images using semantic similarity measures: A comparative study

Abstract

In the last decade, the increasing popularity of image sharing applications over the web has led to a huge rising in the size of the personal image collections. While conventional content-based image retrieval systems suffer from the commonly acknowledged semantic difference between low-level image features and high-level semantics, using textual information associated with images could be a good alternative. Therefore, in order to facilitate the navigation through these collections, and extracting meaningful information from them rapidly and accurately, semantic clustering of images based on textual information could help performing such an important task. In this study, we present a comparative study of several semantic similarity metrics for image datasets clustering. In particular, we evaluate the performance of eight measures namely Path, Resnik, Wu-Palmer, Lin, Jiang-Conrath, Leacock-Chodorow, Li, and Wpath. We conduct our experiments on three public datasets. The experimental results revealed that Resnik and Wpath Similarity measures whith accuracy (78% and 77.67% respectively) outperform the remaining metrics and yield more coherent and fast clustering solutions.