Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.
In this paper, we present a fully-automated system for place recognition at a city-scale based on content-based image retrieval.
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks.
Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems.
Medical Image Retrieval is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content.
The idea of neural codes, based on fully connected layers activations, is extended by incorporating the information contained in convolutional layers.
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history.
However, in a previous study, we have shown that binary auxiliary tasks are inferior to the usage of a rough similarity estimate that are derived from data annotations.