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
Multidimensional binary search trees used for associative searching
Communications of the ACM
Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Towards Data-Adaptive and User-Adaptive Image Retrieval by Peer Indexing
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
FISH: a practical system for fast interactive image search in huge databases
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A Bayesian Approach to Hybrid Image Retrieval
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
PAGER: parameterless, accurate, generic, efficient kNN-based regression
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Hi-index | 0.00 |
In this paper, we consider the problem of finding the k most similar objects given a query object, in large multimedia datasets. We focus on scenarios where the similarity measure itself is not fixed, but is continuously being refined with user feedback. Conventional database techniques for efficient similarity search are not effective in this environment as they take a specific similarity/distance measure as input and build index structures tuned for that measure. Our approach works effectively in this environment as validated by the experimental study where we evaluate it over a wide range of datasets. The experiments show it to be efficient and scalable. In fact, on all our datasets, the response times were within a few seconds, making our approach suitable for interactive applications.