Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for Multi-feature Image Retrieval: A Manifold Aspect

TL;DR

This paper introduces a novel multi-feature image retrieval method using a Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking, improving retrieval accuracy by leveraging manifold structure and multiple features.

Contribution

It proposes the TTNG and MFR methods for enhanced multi-feature image retrieval, considering manifold structure and feature correlation, outperforming existing methods.

Findings

Achieved N-S score of 3.91 on UK-bench.

Attained 65% precision on Corel-10K.

Outperformed state-of-the-art methods in experiments.

Abstract

Single feature is inefficient to describe content of an image, which is a shortcoming in traditional image retrieval task. We know that one image can be described by different features. Multi-feature fusion ranking can be utilized to improve the ranking list of query. In this paper, we first analyze graph structure and multi-feature fusion re-ranking from manifold aspect. Then, Three Tiers Neighborhood Graph (TTNG) is constructed to re-rank the original ranking list by single feature and to enhance precision of single feature. Furthermore, we propose Multi-graph Fusion Ranking (MFR) for multi-feature ranking, which considers the correlation of all images in multiple neighborhood graphs. Evaluations are conducted on UK-bench, Corel-1K, Corel-10K and Cifar-10 benchmark datasets. The experimental results show that our TTNG and MFR outperform than other state-of-the-art methods. For example, we achieve competitive results N-S score 3.91 and precision 65.00% on UK-bench and Corel-10K datasets respectively.