The BANG file: A new kind of grid file
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
The buddy tree: an efficient and robust access method for spatial data base
Proceedings of the sixteenth international conference on Very large databases
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
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A comparison of spatial query processing techniques for native and parameter spaces
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A retrieval technique for similar shapes
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity searching in large image database
Similarity searching in large image database
Feature-index-based similar shape retrieval
Proceedings of the third IFIP WG2.6 working conference on Visual database systems 3 (VDB-3)
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
An effective way to represent quadtrees
Communications of the ACM
Robot Vision
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases
Proceedings of the Fifth International Conference on Data Engineering
Feature-Based Retrieval of Similar Shapes
Proceedings of the Ninth International Conference on Data Engineering
Proceedings of the Ninth International Conference on 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
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Resemblance and Symmetries of Geometric Patterns
Data Structures and Efficient Algorithms, Final Report on the DFG Special Joint Initiative
Proceedings of the Sixth International Conference on Data Engineering
S3: similarity search in CAD database systems
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Clustering techniques for large data sets—from the past to the future
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
Fast and Effective Retrieval of Medical Tumor Shapes
IEEE Transactions on Knowledge and Data Engineering
Dynamically Optimizing High-Dimensional Index Structures
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Efficient retrieval of similar shapes
The VLDB Journal — The International Journal on Very Large Data Bases
Using sets of feature vectors for similarity search on voxelized CAD objects
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Visualizing geographic information: VisualPoints vs CartoDraw
Information Visualization
CartoDraw: A Fast Algorithm for Generating Contiguous Cartograms
IEEE Transactions on Visualization and Computer Graphics
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
XMage: an image retrieval method based on partial similarity
Information Processing and Management: an International Journal
Business process impact visualization and anomaly detection
Information Visualization
Structure-oriented contour representation and matching for engineering shapes
Computer-Aided Design
Partial Similarity of Objects, or How to Compare a Centaur to a Horse
International Journal of Computer Vision
3D protein structure matching by patch signatures
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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In this paper, we introduce the concept of extended feature objects for similarity retrieval. Conventional approaches for similarity search in databases map each object in the database to a point in some high-dimensional feature space and define similarity as some distance measure in this space. For many similarity search problems, this feature-based approach is not sufficient. When retrieving partially similar polygons, for example, the search cannot be restricted to edge sequences, since similar polygon sections may start and end anywhere on the edges of the polygons. In general, inherently continuous problems such as the partial similarity search cannot be solved by using point objects in feature space. In our solution, we therefore introduce extended feature objects consisting of an infinite set of feature points. For an efficient storage and retrieval of the extended feature objects, we determine the minimal bounding boxes of the feature objects in multidimensional space and store these boxes using a spatial access structure. In our concrete polygon problem, sets of polygon sections are mapped to 2D feature objects in high-dimensional space which are then approximated by minimal bounding boxes and stored in an R $^*$-tree. The selectivity of the index is improved by using an adaptive decomposition of very large feature objects and a dynamic joining of small feature objects. For the polygon problem, translation, rotation, and scaling invariance is achieved by using the Fourier-transformed curvature of the normalized polygon sections. In contrast to vertex-based algorithms, our algorithm guarantees that no false dismissals may occur and additionally provides fast search times for realistic database sizes. We evaluate our method using real polygon data of a supplier for the car manufacturing industry.