Adaptive Weighting Feature Aggregation using Particle Swarm Optimization for Image Retrieval

Obtaining robust representational features is essential for achieving high performance in image retrieval. One effective strategy to boost the representational capacity of deep features is through feature aggregation, a technique that combines features from various feature maps of a deep network. In this paper, we propose a novel feature aggregation method that integrates feature maps from different levels of abstraction within a deep network into a single feature vector. The proposed method employs the particle swarm optimization algorithm to adaptively assign optimal weights to each group of feature maps. Extensive experiments are conducted to validate the effectiveness of our proposed feature aggregation method. It is shown that the proposed method significantly outperforms existing state-of-the-art feature aggregation methods on various benchmark datasets.