DMCA
SHOSLIF-O: SHOSLIF for Object Recognition and Image Retrieval (Phase II) (1995)
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BibTeX
@MISC{Swets95shoslif-o:shoslif,
author = {Daniel Swets},
title = {SHOSLIF-O: SHOSLIF for Object Recognition and Image Retrieval (Phase II)},
year = {1995}
}
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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. 1 Introduction The ability of computers to rapidly and successfully retrieve information from image databases based on the objects contained in the images has a direct impact on the progress of d...
Keyphrases
phase ii image retrieval object recognition logarithmic retrieval complexity large image database real-world object natural setting self-organizing framework self-organizing hierarchical optimal subspace learning automatic optimal feature selection direct impact image database content-based retrieval hierarchical image database organization large variability optimal linear projection large database incremental learning query technique open-ended learning problem particular object class inference framework
