Structural Indexing: Efficient 2D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
Fast parallel similarity search in multimedia databases
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Extended attributed string matching for shape recognition
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
VLDB '98 Proceedings of the 24rd 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
Fast retrieval of isolated visual shapes
Computer Vision and Image Understanding
Fast correspondence-based system for shape retrieval
Pattern Recognition Letters
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
Successive elimination algorithm for motion estimation
IEEE Transactions on Image Processing
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To provide satisfactory accuracy and flexibility, most of the existing shape retrieval methods make use of different alignments and translations of the objects that introduce much computational complexity. The most computationally expensive part of these algorithms is measuring the degree of match (or mismatch) of the query object with the objects stored in database. In this paper, we present an approach to cut down a large portion of this search space (number of objects in database) that retrieval algorithms need to take into account. This method is applicable in clustering based approaches also. Moreover, this minimization is done keeping the accuracy of the retrieval algorithms intact and its efficiency is not severely affected in high dimensionalities.