A Texture Thesaurus for Browsing Large
Aerial Photographs

Wei-Ying Ma and B. S. Manjunath
Department of Electrical and Computer Engineering,
University of California, Santa Barbara, CA 93106-9560.
E-mail: wei@iplab.ece.ucsb.edu; manj@ece.ucsb.edu

Abstract

A texture-based image retrieval system for browsing
large-scale aerial photographs is presented. The salient
components of this system include texture feature extraction,
image segmentation and grouping, learning
similarity measure, and a texture thesaurus model for
fast search and indexing. The texture features are computed
by filtering the image with a bank of Gabor filters.
This is followed by a texture gradient computation to
segment each large airphoto into homogeneous regions.
A hybrid neural network algorithm is used to learn the
visual similarity by clustering patterns in the feature
space. With learning similarity, the retrieval performance
improves significantly. Finally, a texture image thesaurus
is created by combining the learning similarity algorithm
with a hierarchical vector quantization scheme. This thesaurus
facilitates the indexing process while maintaining
a good retrieval performance. Experimental results demonstrate
the robustness of the overall system in searching
over a large collection of airphotos and in selecting
a diverse collection of geographic features such as
housing developments, parking lots, highways, and airports.