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Extending the bag-of-words representation with neighboring local features and deep convolutional features

: Manger, Daniel; Willersinn, Dieter

Volltext urn:nbn:de:0011-n-4701472 (373 KByte PDF)
MD5 Fingerprint: 3882eced63fe0165687194e8abbd7cb3
Erstellt am: 24.10.2017

McDonald, John (Ed.) ; Irish Pattern Recognition & Classification Society -IPRCS-:
IMVIP 2017, 19th Irish Machine Vision and Image Processing. Conference Proceedings : 30th August - 1st September 2017, Maynooth University, Co. Kildare, Ireland
Galway: IPRCS, 2017
ISBN: 978-0-9934207-2-6
Irish Machine Vision and Image Processing (IMVIP) <19, 2017, Kildare>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
content-based image retrieval; Bag-of-Words; spatial context of local feature

In this work, we propose and compare two methods to extend the bag-of-words representation which is still widely used in the domain of content-based image retrieval where a query image is used to search for those images in a large image database that show the same object or scene. To this end, typically, local features such as SIFT are quantized and treated independently to leverage an inverted file indexing scheme for speedup. As the quantization of local features impairs their discriminability, the ability to retrieve the relevant database images is decreasing in larger databases. We address this issue by extending every quantized local feature with information from its local spatial neighborhood. More precisely, we make use of two approaches widely used for global image features: the Fisher Vector representation aggregating the neighboring local features and a representation based on pooling features from deep convolutional neural network layer outputs. Using four public datasets, we evaluate the representations in terms of their performance after quantization.