Algorithms for clustering data
Algorithms for clustering data
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image indexing & retrieval using intermediate features
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
ACM Computing Surveys (CSUR)
Information Retrieval
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Adaptive Query Processing: A Survey
BNCOD 19 Proceedings of the 19th British National Conference on Databases: Advances in Databases
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A case study on array query optimisation
Proceedings of the 1st international workshop on Computer vision meets databases
Proceedings of the 1st international workshop on Computer vision meets databases
Proceedings of the 16th International Conference on Extending Database Technology
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The typical mode for querying in an image content-based information system is query-by-example, which allows the user to provide an image as a query and to search for similar images (i.e., the nearest neighbors) based on one or a combination of low-level multidimensional features of the query example. Off-lime, this requires the time-consuming pre-computing of the whole set of visual descriptors over the image database. On-line, one major drawback is that multidimensional sequential NN-search is usually exhaustive over the whole image set face to the user who has a very limited patience. In this paper, we propose a technique for improving the performance of image query-by-example execution strategies over multiple visual features. This includes first, the pre-clustering of the large image database and then, the scheduling of the processing of the feature clusters before providing progressively the query results (i.e., intermediate results are sent continuously before the end of the exhaustive scan over the whole database). A cluster eligibility criterion and two filtering rules are proposed to select the most relevant clusters to a query-by-example. Experiments over more than 110 000 images and five MPEG-7 global features show that our approach significantly reduces the query time in two experimental cases: the query time is divided by 4.8 for 100 clusters per descriptor type and by 7 for 200 clusters per descriptor type compared to a "blind" sequential NN-search with keeping the same final query result. This constitutes a promising perspective for optimizing image query-by-example execution.