Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets

TL;DR

This paper introduces a novel, efficient multi-feature fusion method for image retrieval that dynamically selects neighbors, constructs graphs with constrained dominant sets, and computes feature impact weights to enhance retrieval accuracy.

Contribution

It proposes a new unsupervised fusion approach using constrained dominant sets and entropy-based impact weights, improving image retrieval performance.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively combines handcrafted and deep features.

Reduces false matches through graph-based analysis.

Abstract

Aggregating different image features for image retrieval has recently shown its effectiveness. While highly effective, though, the question of how to uplift the impact of the best features for a specific query image persists as an open computer vision problem. In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner. First, we introduce an incremental nearest neighbor (NN) selection method, whereby we dynamically select k-NN to the query. We then build several graphs from the obtained NN sets and employ constrained dominant sets (CDS) on each graph G to assign edge weights which consider the intrinsic manifold structure of the graph, and detect false matches to the query. Finally, we elaborate the computation of feature positive-impact weight (PIW) based on the dispersive degree of the characteristics vector. To this end, we exploit the entropy of a cluster membership-score distribution. In addition, the final NN set bypasses a heuristic voting scheme. Experiments on several retrieval benchmark datasets show that our method can improve the state-of-the-art result.