Random Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation

Abstract Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. This stresses the need for computer vision methods that automate image retrieval, classification, and annotation tasks.