Automatic Query Image Disambiguation for Content-Based Image Retrieval

TL;DR

This paper introduces a minimal-interaction method for disambiguating query images in content-based image retrieval by clustering neighborhoods and re-ranking based on user feedback, significantly improving retrieval precision.

Contribution

It presents a novel approach combining clustering and feedback integration to effectively resolve image ambiguity with limited user input.

Findings

Achieved a 23% relative improvement in average precision on MIRFLICKR-25K.

Effectively disambiguates query images by clustering neighborhoods.

Enhances retrieval accuracy with minimal user interaction.

Abstract

Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve this, the neighborhood of the query image is divided into coherent clusters from which the user may choose the relevant ones. A novel feedback integration technique is then employed to re-rank the entire database with regard to both the user feedback and the original query. We evaluate our approach on the publicly available MIRFLICKR-25K dataset, where it leads to a relative improvement of average precision by 23% over the baseline retrieval, which does not distinguish between different image senses.