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Large Scale Image Retrieval and Its Challenges

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Composing Fisher Kernels from Deep Neural Models

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

Fisher vector encoding from deep architectures has shown significant improvement in the performance of classification and retrieval tasks. Despite significant benefit of Fisher vectors for classification and retrieval problems, they suffer from the problem of high dimensionality giving rise to computational and storage overhead for large scale learning problems. This chapter provides guidelines for tackling this issue by either deploying feature selection or compression methods. We provide an overview of all the popular feature selection and compression techniques and identify some metrics that can help practitioners identify the appropriateness of each technique used for the cause.

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Authors and Affiliations

  1. Center of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan

    Tayyaba Azim

  2. Institute of Management Sciences, Peshawar, Pakistan

    Sarah Ahmed

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  1. Tayyaba Azim
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  2. Sarah Ahmed
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Correspondence to Tayyaba Azim .

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Azim, T., Ahmed, S. (2018). Large Scale Image Retrieval and Its Challenges. In: Composing Fisher Kernels from Deep Neural Models. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-98524-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-98524-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98523-7

  • Online ISBN: 978-3-319-98524-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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