A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial image retrieval, identification, and inference system
MULTIMEDIA '93 Proceedings of the first ACM international conference on Multimedia
Multirate systems and filter banks
Multirate systems and filter banks
Quad-tree segmentation for texture-based image query
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Compressing still and moving images with wavelets
Multimedia Systems - Special issue on video compression
Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geographical image classification and retrieval
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Semantic clustering and querying on heterogeneous features for visual data
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Supporting Content-Based Retrieval in Large Image Database Systems
Multimedia Tools and Applications
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
An Intelligent Image Database System
IEEE Transactions on Software Engineering
A Visual Information Management System for the Interactive Retrieval of Faces
IEEE Transactions on Knowledge and Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Webview: a distributed geographical image retrieval system
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
| Hi-index | 0.00 |
Current retrieval methods in geographic image databases use only pixel-by-pixel spectral information. Texture is an important property of geographical images that can improve retrieval effectiveness and efficiency. In this paper, we present a content-based retrieval approach that utilizes the texture features of geographical images. Various texture features are extracted using wavelet transforms. Based on the texture features, we design a hierarchical approach to cluster geographical images for effective and efficient retrieval, measuring distances between feature vectors in the feature space. Using wavelet-based multi-resolution decomposition, two different sets of texture features are formulated for clustering. For each feature set, different distance measurement techniques are designed and experimented for clustering images in a database. The experimental results demonstrate that the retrieval efficiency and effectiveness improve when our clustering approach is used.