Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Principles of multimedia database systems
Principles of multimedia database systems
Spatial Color Indexing and Applications
International Journal of Computer Vision
Geostatistical classification for remote sensing: an introduction
Computers & Geosciences
Content-Based Image Database Retrieval Using Variances of Gray Level Spatial Dependencies
MINAR '98 Proceedings of the IAPR International Workshop on Multimedia Information Analysis and Retrieval
Histogram refinement for content-based image retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
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This research proposes a rotation- and flip-invariant algorithm for representing spatial continuity information in high-resolution geographic images for content based image retrieval (CBIR). Starting with variogram concept, the new visual property representation, in the form of a numeric index vector, consists of a set of semi-variances at selected lags and directions, based on three well-justified principles: (1) capture the basic shape of sample variogram, (2) represent the spatial continuity anisotropy, and (3) make the representation rotation- and flip-invariant. The algorithm goes through two tests. The first test confirms that it can indeed align the image representations based on spatial continuity information of objects within images by re-ordering the semi-variances accordingly. In the second test, the algorithm is applied to retrieve seven types of typical geographic entities from an Erie County orthophoto database. The retrieval results demonstrate the effectiveness of the new algorithm in CBIR, as assessed by retrieval precision.