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Author
Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo
Title
Local pyramidal descriptors for image recognition
Type
Journal Article
Year
2014
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abbreviated Journal
TPAMI
Volume
36
Issue
5
Pages
1033 - 1040
Keywords
Object categorization; local features; kernel methods
Abstract
In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain
further improvement.We achieve state-of-the-art results on Caltech-101
(80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines.
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0162-8828
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Notes
LAMP; 600.079
Approved
no
Call Number
Admin @ si @ SSB2014
Serial
2524
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