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Title: An empirical study on the combination of surf features with VLAD vectors for image search
Author(s): E. Spyromitros-Xioufis, S. Papadopoulos, I. Kompatsiaris, G. Tsoumakas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (4 pages).
Keywords: VLAD, SURF, Image representation, Feature extraction, Principal Component Analysis.
Appeared in: 13th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), IEEE, 2012.
Abstract: The study of efficient image representations has attracted significant interest due to the computational needs of large-scale applications. In this paper we study the performance of the recently proposed VLAD method for aggregating local image descriptors when combined with SURF features, in the domain of image search. The experiments show that when SURF features are used as local image descriptors, VLAD attains better performance compared to using SIFT features. We also study how the average number of local image descriptors extracted per image affects the performance and show that by controlling this number we are able to adjust the trade off between feature extraction time and search accuracy. Finally, we examine the retrieval performance of the proposed scheme with varying levels of distractor images.
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