Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Name
1603.04595.pdf
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2.9 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Morère, Olivier
Lin, Jie
Veillard, Antoine
Duan, Ling-Yu
Chandrasekhar, Vijay
Poggio, Tomaso A
Date Issued
June 2017
Journal
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval (ICMR '17)
Publisher
Association for Computing Machinery (ACM)
Citation
Morère, Olivier et al. “Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval.” Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval (ICMR ’17), June 6-9 2017, Bucharest, Romania, Association for Computing Machinery (ACM), June 2017 © 2017 Association for Computing Machinery (ACM)
Version
Original manuscript
Abstract
The goal of this work is the computation of very compact binary hashes for image instance retrieval. Our approach has two novel contributions. The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks. NIP is able to produce compact and well-performing descriptors with visual representations extracted from convolutional neural networks. We specifically incorporate scale, translation and rotation invariances but the scheme can be extended to any arbitrary sets of transformations. We also show that using moments of increasing order throughout nesting is important. The NIP descriptors are then hashed to the target code size (32-256 bits) with a Restricted Boltzmann Machine with a novel batch-level reg-ularization scheme specifically designed for the purpose of hashing (RBMH). A thorough empirical evaluation with state-of-the-art shows that the results obtained both with the NIP descriptors and the NIP+RBMH hashes are consistently outstanding across a wide range of datasets.
MIT Department
McGovern Institute for Brain Research at MIT. Center for Brains, Minds, and Machines
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Laboratory for Computational and Statistical Learning
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/3078971.3078987
