Accurate and Fast Pixel Retrieval with Spatial and Uncertainty Aware Hypergraph Diffusion

TL;DR

This paper introduces a hypergraph diffusion framework with community selection for efficient and accurate image and pixel retrieval, outperforming existing methods in accuracy and speed.

Contribution

The paper proposes a novel hypergraph-based diffusion method with community selection to improve spatial information propagation and uncertainty assessment in retrieval tasks.

Findings

Achieves state-of-the-art accuracy on (P)ROxford and (P)RParis datasets.

Demonstrates significant improvements over existing diffusion techniques.

Maintains high processing speed while enhancing retrieval precision.

Abstract

This paper presents a novel method designed to enhance the efficiency and accuracy of both image retrieval and pixel retrieval. Traditional diffusion methods struggle to propagate spatial information effectively in conventional graphs due to their reliance on scalar edge weights. To overcome this limitation, we introduce a hypergraph-based framework, uniquely capable of efficiently propagating spatial information using local features during query time, thereby accurately retrieving and localizing objects within a database. Additionally, we innovatively utilize the structural information of the image graph through a technique we term "community selection". This approach allows for the assessment of the initial search result's uncertainty and facilitates an optimal balance between accuracy and speed. This is particularly crucial in real-world applications where such trade-offs are often necessary. Our experimental results, conducted on the (P)ROxford and (P)RParis datasets, demonstrate the significant superiority of our method over existing diffusion techniques. We achieve state-of-the-art (SOTA) accuracy in both image-level and pixel-level retrieval, while also maintaining impressive processing speed. This dual achievement underscores the effectiveness of our hypergraph-based framework and community selection technique, marking a notable advancement in the field of content-based image retrieval.