A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval
It addresses retrieval problems in remote sensing, but appears incremental as it builds on existing graph-based and deep learning approaches.
The paper tackles multi-label remote sensing image retrieval by proposing a graph-theoretic deep representation learning method that extracts multi-label co-occurrence relationships, showing effectiveness compared to state-of-the-art methods.
This paper presents a novel graph-theoretic deep representation learning method in the framework of multi-label remote sensing (RS) image retrieval problems. The proposed method aims to extract and exploit multi-label co-occurrence relationships associated to each RS image in the archive. To this end, each training image is initially represented with a graph structure that provides region-based image representation combining both local information and the related spatial organization. Unlike the other graph-based methods, the proposed method contains a novel learning strategy to train a deep neural network for automatically predicting a graph structure of each RS image in the archive. This strategy employs a region representation learning loss function to characterize the image content based on its multi-label co-occurrence relationship. Experimental results show the effectiveness of the proposed method for retrieval problems in RS compared to state-of-the-art deep representation learning methods. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/GT-DRL-CBIR .