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Erschienen in:
BFST_ED: A Novel Upper Bound Computation Framework for the Graph Edit Distance Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Deep Permutations: Deep Convolutional Neural Networks and Permutation-Based Indexing

verfasst von : Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, Lucia Vadicamo

Erschienen in: Similarity Search and Applications

Verlag: Springer International Publishing

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Abstract

The activation of the Deep Convolutional Neural Networks hidden layers can be successfully used as features, often referred as Deep Features, in generic visual similarity search tasks.
Recently scientists have shown that permutation-based methods offer very good performance in indexing and supporting approximate similarity search on large database of objects. Permutation-based approaches represent metric objects as sequences (permutations) of reference objects, chosen from a predefined set of data. However, associating objects with permutations might have a high cost due to the distance calculation between the data objects and the reference objects.
In this work, we propose a new approach to generate permutations at a very low computational cost, when objects to be indexed are Deep Features. We show that the permutations generated using the proposed method are more effective than those obtained using pivot selection criteria specifically developed for permutation-based methods.

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Vorheriges Kapitel Quantifying the Invariance and Robustness of Permutation-Based Indexing Schemes
Nächstes Kapitel Patch Matching with Polynomial Exponential Families and Projective Divergences
Fußnoten
2
In reality, the number of dimensions is 4,096 or more.
 
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Metadaten
Titel
Deep Permutations: Deep Convolutional Neural Networks and Permutation-Based Indexing
verfasst von
Giuseppe Amato
Fabrizio Falchi
Claudio Gennaro
Lucia Vadicamo
Copyright-Jahr
2016
Buch
Similarity Search and Applications

Print ISBN: 978-3-319-46758-0

Electronic ISBN: 978-3-319-46759-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46759-7_7

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