Text-Guided Alternative Image Clustering

• URL: http://arxiv.org/abs/2406.18589v1
• Date: Fri, 7 Jun 2024 08:37:57 GMT
• Title: Text-Guided Alternative Image Clustering
• Authors: Andreas Stephan, Lukas Miklautz, Collin Leiber, Pedro Henrique Luz de Araujo, Dominik Répás, Claudia Plant, Benjamin Roth,
• Abstract summary: This work explores the potential of large vision-language models to facilitate alternative image clustering. We propose Text-Guided Alternative Image Consensus Clustering (TGAICC), a novel approach that leverages user-specified interests via prompts. TGAICC outperforms image- and text-based baselines on four alternative image clustering benchmark datasets.
• Score: 11.103514372355088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clustering. This work explores the potential of large vision-language models to facilitate alternative image clustering. We propose Text-Guided Alternative Image Consensus Clustering (TGAICC), a novel approach that leverages user-specified interests via prompts to guide the discovery of diverse clusterings. To achieve this, it generates a clustering for each prompt, groups them using hierarchical clustering, and then aggregates them using consensus clustering. TGAICC outperforms image- and text-based baselines on four alternative image clustering benchmark datasets. Furthermore, using count-based word statistics, we are able to obtain text-based explanations of the alternative clusterings. In conclusion, our research illustrates how contemporary large vision-language models can transform explanatory data analysis, enabling the generation of insightful, customizable, and diverse image clusterings.

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