Image retrieval using contrastive weight aggregation histograms

Aggregating deep convolutional features for image retrieval has obtained excellent results in recent years; however, exploiting the several advantages of deep convolutional feature maps remains challenging. To address this problem, we propose a novel weighting method called the contrastive weight aggregation histogram to distinguish the slightly distinguishable and highly distinguishable features in deep convolutional features maps for image retrieval. The main highlights of this paper are as follows: (1) a contrastive weighting is proposed to represent the differential contribution of the slightly and highly distinguishable features. It can enhance the distinguishable information and further improve the representative power of deep convolutional features. (2) A novel method is introduced to generate contrastive weighting by comparing the slightly and highly distinguishable feature aggregation. It has the ability to exploit the several advantages of deep convolutional feature maps in terms of differentiating. Experiments demonstrated that the proposed contrastive weighting method outperforms methods that use the deep convolutional feature aggregation on five benchmark datasets.(c) 2022 Elsevier Inc. All rights reserved.