Using texture to analyze and manage large collections of remote sensed image and video data
- 10 January 2004
- journal article
- Published by Optica Publishing Group in Applied Optics
- Vol. 43 (2), 210-217
- https://doi.org/10.1364/ao.43.000210
Abstract
We describe recent research into using the visual primitive of texture to analyze and manage large collections of remote sensed image and video data. Texture is regarded as the spatial dependence of pixel intensity. It is characterized by the amount of dependence at different scales and orientations, as measured with frequency-selective filters. A homogeneous texture descriptor based on the filter outputs is shown to enable (1) content-based image retrieval in large collections of satellite imagery, (2) semantic labeling and layout retrieval in an aerial video management system, and (3) statistical object modeling in geographic digital libraries.Keywords
This publication has 11 references indexed in Scilit:
- Periodicity, directionality, and randomness: Wold features for image modeling and retrievalIeee Transactions On Pattern Analysis and Machine Intelligence, 1996
- Texture features for browsing and retrieval of image dataIeee Transactions On Pattern Analysis and Machine Intelligence, 1996
- Query by image and video content: the QBIC systemComputer, 1995
- Texture analysis and classification with tree-structured wavelet transformIEEE Transactions on Image Processing, 1993
- Texture classification and segmentation using multiresolution simultaneous autoregressive modelsPattern Recognition, 1992
- Complete discrete 2-D Gabor transforms by neural networks for image analysis and compressionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Detecting buildings in aerial imagesComputer Vision, Graphics, and Image Processing, 1988
- Classification of textures using Gaussian Markov random fieldsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
- Mathematical description of the responses of simple cortical cells*Journal of the Optical Society of America, 1980
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973