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distance between descriptors is the greater is the sim-
ilarity between them. The other information like loca-
tions, orientations, scales and contrasts of features are
not used in the matching step. Therefore, we suggest
a method to involve the scale, contrast and orientation
properties in the matching step to determine which of
them play an important role in solving near-duplicate
retrieval tasks. We perform a benchmark study using
the original SIFT−128D features (Lowe, 2004) and
the region compressed SIFT−64D features (Alyosef
and N
¨
urnberger, 2016) to determine whether the in-
fluence of these properties is equivalent for both of
SIFT−128D and RC-SIFT−64D features.
The remainder of this paper is organized as fol-
lows. Section 2 gives an overview of prior work re-
lated with the SIFT and the RC-SIFT algorithms and
image NDR algorithms. Section 3 details the pro-
posed method to truncate the SIFT features and in-
volve the scale, contrast and orientation properties in
matching process. Section 4 presents the evaluation
measures and the settings of our experiments. Sec-
tion 5 discusses the results of experiments. Finally,
Section 6 draws conclusions of this work and dis-
cusses possible future work.
2 RELATED WORK
The SIFT descriptor has been widely used in various
fields of images retrieval, image near-duplicate re-
trieval (Auclair et al., 2006), (Chum et al., 2008), im-
age classification (Nist
`
er and Stew
`
enius, 2006b) and
object recognition (Jiang et al., 2015) due to its ro-
bustness against different kinds of image transforma-
tion, viewpoint change, blurring and scale change. To
overcome the problem of extracting high dimensional
SIFT descriptors (the length of the original SIFT de-
scriptor is 128) various methods have been suggested.
A method to reduce the dimensionality of SIFT de-
scriptor to 96D, 64D or 32D is described in (Khan
et al., 2011). This reduction is done by skipping the
outside edges of the region around the keypoints to
get 96D descriptor and then averaging the outside re-
gions to obtain 64D descriptor. The reduced SIFT
descriptors of forms SIFT−96D, 64D have shown ro-
bust performance against image affine transformation,
viewpoint change and scale change. In (Ke and Suk-
thankar, 2004), Principle Component Analysis is em-
ployed to obtain 64D SIFT descriptors. This approach
is in need of an off-line training stage to compute
the eigenvalue vector for each image databases sep-
arately. In (Alyosef and N
¨
urnberger, 2016), a method
is proposed to compress the SIFT descriptor to get
RC-SIFT64D, 32D or 16D descriptors.
An important issue when SIFT features are ex-
tracted of an image database is that the amount of
the extracted features of various images is not in-
variant. This issue is addressed in (Foo and Sinha,
2007) and it has been solved by ranking the extracted
SIFT features based on their decreased contrast val-
ues. After that, the list of features is pruned based
on a specific number which determines the required
number of features, so that, features which have low
contrast are skipped. To accelerate the matching pro-
cess many methods has been suggested to find the re-
lation between the extracted features such as build-
ing a dictionary of features based on direct clustering
as described in (Li et al., 2014), (Yang and Newsam,
2008), (Grauman and Darrell, 2005) and (Grauman
and Darrell, 2007) or based hierarchical k−means
clustering as described in (Jiang et al., 2015), (Nist
`
er
and Stew
`
enius, 2006b). Hashing functions are used
in (Chum et al., 2008) and (Auclair et al., 2006) to re-
duce the amount of comparisons between the features
of various images. In (Y. Jianchao and Thomas, 2009)
and (Zhang et al., 2013) the sparse coding concept is
used in a further step after applying feature cluster-
ing to accelerate the matching process and to improve
the matching results. In the next section, we explain
our suggested steps to involve the properties of SIFT
features in the matching process.
3 IMAGE NEAR-DUPLICATE
RETRIEVAL UNDER THE
IMPACT OF FEATURE
PROPERTIES
To explain the effect of involving the scale, con-
trast and orientation properties in solving the near-
duplicate retrieval task, we give a short description
of the way of feature extraction in both, original SIFT
and RC-SIFT algorithms. After that, we describe our
idea to truncate the list of SIFT and RC-SIFT features.
Finally, we explain our suggested method to employ
the scale, contrast and rotation in matching process.
3.1 The Concept of the SIFT−128D &
the RC-SIFT Detectors
The extraction of the original SIFT−128D and the
RC-SIFT features has the same initial steps. These
steps begin by building the image space scale or a so
called ”image pyramid”. This image pyramid con-
tains octaves downsampled and scaled copies of an
input image. Based on this pyramid the difference
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