PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Falchi F., Vadicamo L. How effective are aggregation methods on binary features?. In: VISAPP 2016 - International Conference on Computer Vision Theory and Applications (Roma, Italy, 27-29 February 2016). Proceedings, vol. 4 pp. 556 - 573. Nadia Magnenat-Thalmann, Paul Richard, Lars Linsen, Alexandru Telea, Sebastiano Battiato, Francisco Imai, Josť Braz (eds.). SciTePress - Science and Technology Publications, 2016.
 
 
Abstract
(English)
During the last decade, various local features have been proposed and used to support Content Based Image Retrieval and object recognition tasks. Local features allow to effectively match local structures between images, but the cost of extraction and pairwise comparison of the local descriptors becomes a bottleneck when mobile devices and/or large database are used. Two major directions have been followed to improve efficiency of local features based approaches. On one hand, the cost of extracting, representing and matching local visual descriptors has been reduced by defining binary local features. On the other hand, methods for quantizing or aggregating local features have been proposed to scale up image matching on very large scale. In this paper, we performed an extensive comparison of the state-of-the-art aggregation methods applied to ORB binary descriptors. Our results show that the use of aggregation methods on binary local features is generally effective even if, as expected, there is a loss of performance compared to the same approaches applied to non-binary features. However, aggregations of binary feature represent a worthwhile option when one need to use devices with very low CPU and memory resources, as mobile and wearable devices.
URL: http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=+UzaCiMvw6M=&t=1
DOI: 10.5220/0005719905660573
Subject Multimedia information retrieval
Image representation
Binary local features
CBIR
Bag of word
VLAD
Fisher vector
H.3.3 Information Search and Retrieval


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