Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
This work addresses image retrieval for computer vision applications, offering an unsupervised approach that outperforms existing methods, though it appears incremental as it builds on deep convolutional features and aggregation techniques.
The paper tackles the problem of image retrieval by proposing an unsupervised part-based weighting aggregation (PWA) method that uses deep convolutional filters as part detectors to highlight discriminative object parts and suppress background noise, achieving state-of-the-art performance on four standard datasets.
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. Code is available at https://github.com/XJhaoren/PWA.