Several recent works have shown that part-based image representation provides state-of-the-art performance for fine-grained categorization. Moreover, it has also been shown that image global representation generated by aggregating deep convolutional features provides excellent performance for image retrieval. In this paper we propose a novel aggregation method, which utilizes the information of retrieval object parts. The proposed part-based weighting aggregation (PWA) method utilizes the normalized feature maps as part detectors to weight and aggregate the convolutional features. The part detectors which are selected by the unsupervised method highlight the discriminative parts of objects and effectively suppress the noise of background. We experiment on five public standard datasets for image retrieval. Our unsupervised PWA method outperforms the state-of-the-art approaches based on pre-trained networks and achieves comparable accuracy with the fine-tuned methods. It is worth noting that our unsupervised method is very suitable and effective for the situation where the annotated training dataset is difficult to collect.

本文提出了一种基于语义部分加权聚合（PWA）的图像检索方法，利用深度卷积层的具有区别性的过滤器作为部分检测器，并通过有效的无监督策略选择一些部分检测器生成概率建议，最终实现了对视觉对象的全局加权表示。研究表明，与现有的无监督和有监督的聚合方法相比，本文提出的无监督PWA方法具有更好的性能。

基于部件的无监督深度卷积特征加权聚合在图像检索中的应用