L
Laurent Amsaleg
Researcher at University of Rennes
Publications - 122
Citations - 3242
Laurent Amsaleg is an academic researcher from University of Rennes. The author has contributed to research in topics: Search engine indexing & Image retrieval. The author has an hindex of 25, co-authored 117 publications receiving 2979 citations. Previous affiliations of Laurent Amsaleg include Centre national de la recherche scientifique & University of Chicago.
Papers
More filters
Proceedings ArticleDOI
Evaluation of GIST descriptors for web-scale image search
TL;DR: This paper evaluates the search accuracy and complexity of the global GIST descriptor for two applications, for which a local description is usually preferred: same location/object recognition and copy detection, and proposes an indexing strategy for global descriptors that optimizes the trade-off between memory usage and precision.
Journal ArticleDOI
Locality sensitive hashing: A comparison of hash function types and querying mechanisms
TL;DR: This paper compares several families of space hashing functions in a real setup and reveals that unstructured quantizer significantly improves the accuracy of LSH, as it closely fits the data in the feature space.
Proceedings ArticleDOI
Searching in one billion vectors: Re-rank with source coding
TL;DR: This paper releases a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale and accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.
Proceedings ArticleDOI
Cost-based query scrambling for initial delays
TL;DR: Three different approaches to using query optimization for scrambling are proposed and it is shown that cost-based scrambling can effectively hide initial delays, but that in the absence of good predictions of expected delay durations, there are fundamental tradeoffs between risk aversion and effectiveness.
Proceedings ArticleDOI
Scrambling query plans to cope with unexpected delays
TL;DR: An algorithm is presented that modifies execution plans on-the-fly in response to unexpected delays in obtaining initial requested tuples from remote sources using a class of dynamic, run time query plan modification techniques that are called query plan scrambling.