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Compression Techniques for Deep Fisher Vectors

Topics: Classification; Computational Learning Theory; Embedding and Manifold Learning; Feature Selection and Extraction; ICA, PCA, CCA and other Linear Models; Image Understanding; Information Retrieval; Kernel Methods; Model Selection; Neural Networks; Object Recognition; Signal Processing; Sparsity

Authors: Sarah Ahmed and Tayyaba Azim

Affiliation: Institute of Management Sciences, Pakistan

ISBN: 978-989-758-222-6

Keyword(s): Fisher Vectors (FV), Restricted Boltzmann Machine (RBM), k-Nearest Neighbor (k-nn), Principal Component Analysis (PCA), Parametric t-SNE, Spectral Hashing.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Classification ; Computational Intelligence ; Computational Learning Theory ; Computer Vision, Visualization and Computer Graphics ; Data Engineering ; Embedding and Manifold Learning ; Feature Selection and Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; ICA, PCA, CCA and other Linear Models ; Image Understanding ; Information Retrieval ; Kernel Methods ; Methodologies and Methods ; Model Selection ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Object Recognition ; Ontologies and the Semantic Web ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Sparsity ; Theory and Methods

Abstract: This paper investigates the use of efficient compression techniques for Fisher vectors derived from deep architectures such as Restricted Boltzmann machine (RBM). Fisher representations have recently created a surge of interest by proving their worth for large scale object recognition and retrieval problems, however they suffer from the problem of large dimensionality as well as have some intrinsic properties that make them unique from the conventional bag of visual words (BoW) features. We have shown empirically which of the normalisation and state of the art compression techniques is well suited for deep Fisher vectors making them amenable for large scale visual retrieval with reduced memory footprint. We further show that the compressed Fisher vectors give impressive classification results even with costless linear classifiers like k-nearest neighbour.

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Paper citation in several formats:
Ahmed S. and Azim T. (2017). Compression Techniques for Deep Fisher Vectors.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 217-224. DOI: 10.5220/0006205002170224

@conference{icpram17,
author={Sarah Ahmed and Tayyaba Azim},
title={Compression Techniques for Deep Fisher Vectors},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006205002170224},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Compression Techniques for Deep Fisher Vectors
SN - 978-989-758-222-6
AU - Ahmed S.
AU - Azim T.
PY - 2017
SP - 217
EP - 224
DO - 10.5220/0006205002170224

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