Improving the search accuracy of the VLAD through weighted aggregation of local descriptors

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We present a novel compact image descriptor, called the Weighted VLAD (wVLAD), which extends the original vector of locally aggregated descriptors (VLAD). The main idea is that the relative importance of local descriptors can be quite different among the local descriptors of an image, depending on the positions from which the descriptors are extracted. Thus, we propose an approach where we assign a weight to each local descriptor of an image, and then compute weighted aggregations of local descriptors. The weights of local descriptors are measured by performing saliency analysis together with an appropriate calibration function. We show, through experiments on publicly available datasets, that our proposed method works better than other existing methods in most image datasets. (C) 2015 Elsevier Inc. All rights reserved.
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Issue Date
2015-08
Language
English
Article Type
Article
Keywords

IMAGE CLASSIFICATION; SCALE; FEATURES; IDF

Citation

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.31, pp.237 - 252

ISSN
1047-3203
DOI
10.1016/j.jvcir.2015.07.005
URI
http://hdl.handle.net/10203/200447
Appears in Collection
CS-Journal Papers(저널논문)
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