Unsupervised Part-Based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
Jian Xu, Cunzhao Shi, Chengzuo Qi, Chunheng Wang, Baihua Xiao · Proceedings of the AAAI Conference on Artificial Intelligence · 2018
In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval. The proposed PWA utilizes the discriminative filters of deep convolutional layers as part detectors. Moreover, we propose the effective unsupervised strategy to select some part detectors to generate the "probabilistic proposals," which highlight certain discriminative parts of objects and suppress the noise of background. The final global PWA representation could then be acquired by aggregating the regional representations weighted by the selected "probabilistic proposals" corresponding to various semantic content. We conduct comprehensive experiments on four standard datasets and show that our unsupervised PWA outperforms the state-of-the-art unsupervised and supervised aggregation methods.