Multilayer feedforward networks are universal approximators
Neural Networks
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
Introduction to artificial neural systems
Introduction to artificial neural systems
International Journal of Computer Vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Video and image processing in multimedia systems
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BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
VisualSEEk: a fully automated content-based image query system
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Geographical image classification and retrieval
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Fuzzy queries in multimedia database systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Semantic clustering and querying on heterogeneous features for visual data
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
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SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Robot Vision
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IEEE Transactions on Knowledge and Data Engineering
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient Access to and Retrieval from a Shape Image Database
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Color Clustering Techniques for Color-Content-Based Image Retrieval from Image Databases
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
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MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
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Pattern Recognition
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Image and Vision Computing
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MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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The effectiveness of the content-based image retrieval can be enhanced using heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrievals on heterogeneous features is a nontrivial task. In this paper, we present a semantics-based clustering and indexing approach, termed SemQuery, to support visual queries on heterogeneous features of images. Using this approach, the database images are classified based on their heterogeneous features. Each semantic image cluster contains a set of subclusters that are represented by the heterogeneous features that the images contain. An image is included in a semantic cluster if it falls within the scope of all the heterogeneous clusters of the semantic cluster. We also design a neural network model to merge the results of basic queries on individual features. A query processing strategy is then presented to support visual queries on heterogeneous features. An experimental analysis is conducted and presented to demonstrate the effectiveness and efficiency of the proposed approach.