Authors:
Daniel Manger
1
;
Dieter Willersinn
1
and
Jürgen Beyerer
2
Affiliations:
1
Fraunhofer IOSB, Germany
;
2
Fraunhofer IOSB and Karlsruhe Institute of Technology KIT, Germany
Keyword(s):
Content-based Image Retrieval, Bag-of-words, Convolutional Neural Networks, Index.
Abstract:
Facing ever-growing image databases, the focus of research in content-based image retrieval, where a query
image is used to search for those images in a large database that show the same object or scene, has shifted
in the last decade. Instead of using local features such as SIFT together with quantization and inverted file indexing
schemes, models working with global features and exhaustive search have been proposed to encounter
limited main memory and increasing query times. This, however, impairs the capability to find small objects in
images with cluttered background. In this paper, we argue, that it is worth reconsidering image retrieval with
local features because since then, two crucial ingredients became available: large solid-state disks providing
dramatically shorter access times, and more discriminative models enhancing the local features, for example,
by encoding their spatial neighborhood using features from convolutional neural networks resulting in way
fewer r
andom read memory accesses. We show that properly combining both insights renders it possible to
keep the index of the database images on the disk rather than in the main memory which allows even larger
databases on today’s hardware. As proof of concept we support our arguments with experiments on established
public datasets for large-scale image retrieval.
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