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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 3/2016

01.02.2016

Image Search with Selective Match Kernels: Aggregation Across Single and Multiple Images

verfasst von: Giorgos Tolias, Yannis Avrithis, Hervé Jégou

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2016

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Abstract

This paper considers a family of metrics to compare images based on their local descriptors. It encompasses the vector or locally aggregated descriptors descriptor and matching techniques such as hamming embedding. Making the bridge between these approaches leads us to propose a match kernel that takes the best of existing techniques by combining an aggregation procedure with a selective match kernel. The representation underpinning this kernel is approximated, providing a large scale image search both precise and scalable, as shown by our experiments on several benchmarks. We show that the same aggregation procedure, originally applied per image, can effectively operate on groups of similar features found across multiple images. This method implicitly performs feature set augmentation, while enjoying savings in memory requirements at the same time. Finally, the proposed method is shown effective for place recognition, outperforming state of the art methods on a large scale landmark recognition benchmark.
Vorheriger Artikel Geo-localization using Volumetric Representations of Overhead Imagery
Nächster Artikel Erratum to: Image Search with Selective Match Kernels: Aggregation Across Single and Multiple Images

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Fußnoten
1
This is in contrast to our previous work (Tolias et al. 2013), where we have combined ASMK\(^\star \) with the geometry-based variant.
 
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Metadaten
Titel
Image Search with Selective Match Kernels: Aggregation Across Single and Multiple Images
verfasst von
Giorgos Tolias
Yannis Avrithis
Hervé Jégou
Publikationsdatum
01.02.2016
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 3/2016
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-015-0810-4

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