Baggage Image Retrieval with Attention-Based Network for Security Checks

Abstract

Baggage image search is the task of finding the same baggage across several cameras, which can improve the efficiency of security check. Since existing state-of-the-art image retrieval requires a significant amount of training data, we aim to investigate the few-shot situation, utilizing only a few samples for each baggage. In this paper, the framework we introduced is called AttentionNet, exploring the weak region-wise annotations as attention clues to improve the retrieval performance. Specifically, we integrate the semantic and recognition tasks using shared convolutional neural networks by multi-task learning. Semantic attention mechanism is used to explicitly re-weight the feature map for emphasizing the foreground. To prevent overfitting in few-shot training, we adopt a variant of the triplet loss to perform deep metric learning with an online hard triplet mining strategy. Once trained, AttentionNet identifies a probe image by computing its embedding’s cosine distance with images in the gallery. Experiments show that our method achieves state-of-the-art accuracy in image search. For instance, we obtain 82.9% Rank-1 score on the MVB dataset, dramatically outperforming the currently existing methods.