The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform

Hervé Jégou - One of the best experts on this subject based on the ideXlab platform.

  • Visual query expansion with or without geometry: refining local descriptors by feature aggregation
    Pattern Recognition, 2014
    Co-Authors: Giorgos Tolias, Hervé Jégou
    Abstract:

    This paper proposes a query expansion technique for image search that is faster and more precise than the existing ones. An enriched representation of the query is obtained by exploiting the binary representation offered by the Hamming Embedding image matching approach: The initial local descriptors are refined by aggregating those of the database, while new descriptors are produced from the images that are deemed relevant. The technique has two computational advantages over other query expansion techniques. First, the size of the enriched representation is comparable to that of the initial query. Second, the technique is effective even without using any geometry, in which case searching a database comprising 105k images typically takes 80 ms on a Desktop Machine. Overall, our technique significantly outperforms the visual query expansion state of the art on popular benchmarks. It is also the first query expansion technique shown effective on the UKB benchmark, which has few relevant images per query.

  • Local visual query expansion: Exploiting an image collection to refine local descriptors
    2013
    Co-Authors: Giorgos Tolias, Hervé Jégou
    Abstract:

    This paper proposes a query expansion technique for image search that is faster and more precise than the existing ones. An enriched representation of the query is obtained by exploiting the binary representation offered by the Hamming Embedding image matching approach: The initial local descriptors are refined by aggregating those of the database, while new descriptors are produced from the images that are deemed relevant. This approach has two computational advantages over other query expansion techniques. First, the size of the enriched representation is comparable to that of the initial query. Second, the technique is effective even without using any geometry, in which case searching a database comprising 105k images typically takes 79 ms on a Desktop Machine. Overall, our technique significantly outperforms the visual query expansion state of the art on popular benchmarks. It is also the first query expansion technique shown effective on the UKB benchmark, which has few relevant images per query.

Giorgos Tolias - One of the best experts on this subject based on the ideXlab platform.

  • Visual query expansion with or without geometry: refining local descriptors by feature aggregation
    Pattern Recognition, 2014
    Co-Authors: Giorgos Tolias, Hervé Jégou
    Abstract:

    This paper proposes a query expansion technique for image search that is faster and more precise than the existing ones. An enriched representation of the query is obtained by exploiting the binary representation offered by the Hamming Embedding image matching approach: The initial local descriptors are refined by aggregating those of the database, while new descriptors are produced from the images that are deemed relevant. The technique has two computational advantages over other query expansion techniques. First, the size of the enriched representation is comparable to that of the initial query. Second, the technique is effective even without using any geometry, in which case searching a database comprising 105k images typically takes 80 ms on a Desktop Machine. Overall, our technique significantly outperforms the visual query expansion state of the art on popular benchmarks. It is also the first query expansion technique shown effective on the UKB benchmark, which has few relevant images per query.

  • Local visual query expansion: Exploiting an image collection to refine local descriptors
    2013
    Co-Authors: Giorgos Tolias, Hervé Jégou
    Abstract:

    This paper proposes a query expansion technique for image search that is faster and more precise than the existing ones. An enriched representation of the query is obtained by exploiting the binary representation offered by the Hamming Embedding image matching approach: The initial local descriptors are refined by aggregating those of the database, while new descriptors are produced from the images that are deemed relevant. This approach has two computational advantages over other query expansion techniques. First, the size of the enriched representation is comparable to that of the initial query. Second, the technique is effective even without using any geometry, in which case searching a database comprising 105k images typically takes 79 ms on a Desktop Machine. Overall, our technique significantly outperforms the visual query expansion state of the art on popular benchmarks. It is also the first query expansion technique shown effective on the UKB benchmark, which has few relevant images per query.

Cheng Wang - One of the best experts on this subject based on the ideXlab platform.

  • CASES - Computation offloading to save energy on handheld devices: a partition scheme
    Proceedings of the international conference on Compilers architecture and synthesis for embedded systems - CASES '01, 2001
    Co-Authors: Cheng Wang
    Abstract:

    We consider handheld computing devices which are connected to a server (or a powerful Desktop Machine) via a wireless LAN. On such devices, it is often possible to save the energy on the handheld by offloading its computation to the server. In this work, based on profiling information on computation time and data sharing at the level of procedure calls, we construct a cost graph for a given application program. We then apply a partition scheme to statically divide the program into server tasks and client tasks such that the energy consumed by the program is minimized. Experiments are performed on a suite of multimedia benchmarks. Results show considerable energy saving for several programs through offloading.

Ross Harder - One of the best experts on this subject based on the ideXlab platform.

  • Real-time coherent diffraction inversion using deep generative networks
    Scientific reports, 2018
    Co-Authors: Mathew J. Cherukara, Youssef S. G. Nashed, Ross Harder
    Abstract:

    Phase retrieval, or the process of recovering phase information in reciprocal space to reconstruct images from measured intensity alone, is the underlying basis to a variety of imaging applications including coherent diffraction imaging (CDI). Typical phase retrieval algorithms are iterative in nature, and hence, are time-consuming and computationally expensive, making real-time imaging a challenge. Furthermore, iterative phase retrieval algorithms struggle to converge to the correct solution especially in the presence of strong phase structures. In this work, we demonstrate the training and testing of CDI NN, a pair of deep deconvolutional networks trained to predict structure and phase in real space of a 2D object from its corresponding far-field diffraction intensities alone. Once trained, CDI NN can invert a diffraction pattern to an image within a few milliseconds of compute time on a standard Desktop Machine, opening the door to real-time imaging.

Ganesh Vijay - One of the best experts on this subject based on the ideXlab platform.

  • Unsatisfiability Proofs for Weight 16 Codewords in Lam's Problem
    'International Joint Conferences on Artificial Intelligence', 2020
    Co-Authors: Bright Curtis, Cheung, Kevin K. H., Stevens Brett, Kotsireas Ilias, Ganesh Vijay
    Abstract:

    In the 1970s and 1980s, searches performed by L. Carter, C. Lam, L. Thiel, and S. Swiercz showed that projective planes of order ten with weight 16 codewords do not exist. These searches required highly specialized and optimized computer programs and required about 2,000 hours of computing time on mainframe and supermini computers. In 2011, these searches were verified by D. Roy using an optimized C program and 16,000 hours on a cluster of Desktop Machines. We performed a verification of these searches by reducing the problem to the Boolean satisfiability problem (SAT). Our verification uses the cube-and-conquer SAT solving paradigm, symmetry breaking techniques using the computer algebra system Maple, and a result of Carter that there are ten nonisomorphic cases to check. Our searches completed in about 30 hours on a Desktop Machine and produced nonexistence proofs of about 1 terabyte in the DRAT (deletion resolution asymmetric tautology) format.Comment: To appear in Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020