Abstract

We describe a self-organizing framework for content-based retrieval of images from large image databases. The SHOSLIF (Self-organizing Hierarchical Optimal Subspace Learning and Inference Framework) system uses the theories of optimal linear projection for automatic optimal feature selection and a hierarchical image database organization to achieve a logarithmic retrieval complexity. We use incremental learning for large-scale and open-ended learning problems, we and allow for perturbations in the size and position of objects in the images through learning. We demonstrate the query technique on a large database of widely varying real-world objects in natural settings, and show the applicability of the approach even for large variability within a particular object class, including 3D rotation.

Citations