2010
DOI: 10.3837/tiis.2010.06.007
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PPD: A Robust Low-computation Local Descriptor for Mobile Image Retrieval

Abstract: This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, wh… Show more

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Cited by 3 publications
(6 citation statements)
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“…The relative performance among different descriptors is consistent with different feature detectors [13]. Since Hessian-affine detector can detect blob-like points, which are less likely at the positions of depth-difference pixel points and favor local planarity and smoothness assumption [12], it is selected in our experiments.…”
Section: Region Detectormentioning
confidence: 63%
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“…The relative performance among different descriptors is consistent with different feature detectors [13]. Since Hessian-affine detector can detect blob-like points, which are less likely at the positions of depth-difference pixel points and favor local planarity and smoothness assumption [12], it is selected in our experiments.…”
Section: Region Detectormentioning
confidence: 63%
“…There are two major approaches to construct rotation-invariant descriptors. One approach is to rotate the normalized region to align a reference orientation [7,9,10,12,15] and then the feature descriptor is built up. The other is to directly design rotation-invariant descriptor [13,14,16].…”
Section: Related Workmentioning
confidence: 99%
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“…In this regard, LPB does not capture the details of microstructures and it is sensitive to image noise. (Karanwal, 2022) Extracting and classifying local image features is a fundamental challenge in many applications (Liu, Yang, & Huang, 2011) and different approaches have been used to describe local image patterns. Some of the descriptors are Shape Context (Salve & Jondhale, 2010), spin image (Lazebnik, Schmid, & Ponce, 2003), complex filters (Schaffalitzky & Zisserman, 2002), steerable filters (Freeman & Adelson, 1991), moment invariants (Van Gool, Moons, & Ungureanu, 1996), SIFT (Lowe, 2004), and the differential invariants (Koenderink & Van Doorn, 1987).…”
Section: Robust Lbb (Rlbp)mentioning
confidence: 99%