Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Density-based indexing for approximate nearest-neighbor queries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
The LSDh-Tree: An Access Structure for Feature Vectors
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust content-based image searches for copyright protection
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Approximate searches: k-neighbors + precision
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Content-based image retrieval from a large image database
Pattern Recognition
Shape reasoning on mis-segmented and mis-labeled objects using approximated Fisher criterion
Computers and Graphics
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Traditional content-based image retrieval systems typically compute a single descriptor per image based for example on color histograms. The result of a query is in general the images from the database whose descriptors are the closest to the descriptor of the query image. Systems built this way are able to return images that are globally similar to the query image, but can not return images that contain some of the objects that are in the query. As opposed to this traditional coarse-grain recognition scheme, recent advances in image processing make fine-grain image recognition possible, notably by computing local descriptors that can detect similar objects in different images. Obviously powerful, fine-grain recognition in images also changes the retrieval process: instead of submitting a single query to retrieve similar images, multiple queries must be submitted and their partial results must be post-processed before delivering the answer. This paper first presents a family of local descriptors supporting fine-grain image recognition. These descriptors enforce robust recognition, despite image rotations and translations, illumination variations, and partial occlusions. Many multi-dimensional indexes have been proposed to speed-up the retrieval process. These indexes, however, have been mostly designed for and evaluated against databases where each image is described by a single descriptor. While this paper does not present any new indexing scheme, it shows that the three most efficient indexing techniques known today are still too slow to be used in practice with local descriptors because of the changes in the retrieval process.