How effective are aggregation methods on binary features?

Amato G., Falchi F., Vadicamo L

VLAD  Binary local features  H.3.3 Information Search and Retrieval  Multimedia information retrieval  CBIR  Fisher vector  Image representation  Bag of word 

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.

Source: International Conference on Computer Vision Theory and Applications, pp. 556–573, Roma, Italy, 27-29 February 2016

Publisher: SCITEPRESS - Science and Technology Publications, digital library, USA

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