Scalable database indexing and fast image retrieval based on deep learning and hierarchically nested structure applied to remote sensing and plant biology

Abstract

Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identification and high-resolution remote sensing data to evaluate the accuracy and the computational efficiency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures.