Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning

Pattern retrieval is a fundamental challenge in machine learning but is often subject to the problem of gathering enough labeled examples of the target pattern, and also to the computational complexity inherent to the training and the evaluation of complex classifier functions on large databases. In this paper, we propose a hierarchical top-down processing scheme for pattern retrieval in high-volume high-resolution optical satellite image repositories. We learn via a multistage active learning process a cascade of classifiers working each at a certain scale on a patch-based representation of images. At each stage of the hierarchy, we seek to eliminate large parts of images considered as nonrelevant, the purpose being to set the focus at the finest scales on more promising and as spatially limited as possible areas. Our scheme is based on the fact that by reducing the size of the analysis window (i.e., the size of the patch), we better capture the properties of the targeted object. The cascaded hierarchy is introduced to compensate for the extra computational burden incurred by diminishing the size of the patch, which causes an explosion of the number of patches to process. Unlike most other retrieval methods, which require large training sets and costly offline training, we propose a cascaded active learning strategy to build a classifier at each level of the hierarchy, and we provide a new Multiple Instance Learning algorithm to propagate automatically the training examples from one level of the hierarchy to the other. Two study cases are performed for validation. The first is a test on a database of 61-cm resolution QuickBird panchromatic images and the second is an example of temporal pattern retrieval from a database of Synthetic Aperture Radar (SAR) image time series. These tests show that our method achieves a reduction in the number of computations of two orders of magnitude, while keeping the same accuracy level as recent state-of-the-art methods.