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1. Variable-Length Signature for Near-Duplicate Image
Matching
ABSTRACT:
We propose a variable-length signature for near-duplicate image matching in this
paper. An image is represented by a signature, the length of which varies with
respect to the number of patches in the image. A new visual descriptor, viz.,
probabilistic center-symmetric local binary pattern, is proposed to characterize the
appearance of each image patch. Beyond each individual patch, the spatial
relationships among the patches are captured. In order to compute the similarity
between two images, we utilize the earth mover’s distance which is good at
handling variable-length signatures. The proposed image signature is evaluated in
two different applications, i.e., near duplicate document image retrieval and near-
duplicate natural image detection. The promising experimental results demonstrate
the validity and effectiveness of the proposedvariable-length signature.
EXISTING SYSTEM:
 Kim employed the ordinal measures of the discrete cosine transform
coefficients to represent an image. Then the L1 norm was utilized for image
similarity computation.

2.  Liu and Yang built a color difference histogram for an image, which
encoded the color and edge orientations of the image in a uniform
framework. Subsequently, the similarity of two images was computed in
terms of the enhanced Canberra distance.
 Aksoy and Haralick proposed line-angle-ratio statistics and co-occurrence
variances to represent an image which were organized into a feature vector
of 28 dimensions. Then different similarity measures were compared in the
image retrieval scenario.
 Meng et al. first represented an image by a 279D feature vector. For
similarity computation, the enhanced Dynamic Partial Function was
proposed which adaptively activated a different number of features in a
pairwise manner to accommodatethe characteristics of each image pair.
 Chum et al. represented an image based on its color histograms and then
employed Locality Sensitive Hashing (LSH) for fast retrieval. For the sake
of computational efficiency, the vectorial representations were first
embedded into binary codes in some works
DISADVANTAGES OF EXISTING SYSTEM:
 In bag-of-visual-words model, the spatial layout of the visual words is
totally disregarded, which will incur ambiguity during matching.

3. PROPOSED SYSTEM:
 A visual descriptor named Probabilistic Center-symmetric Local Binary
Pattern (PCSLBP) is proposed to depict the patch appearance, which is
flexible in the presence of image distortions. Beyond each individual patch,
we describe the relationships among the patches as well, viz. the distance
between every pair of patches in the image. A weight is also assigned to
each patch to indicate its contribution in identifying the image.
 Given the characteristics of all the patches, the image is represented by a
signature. The superiority of signatures over vectors in representing images
is that the former vary in length across images, indicating the image’s
characteristics.
 To compute the similarity between two images, the Earth Mover’s Distance
is employed in our work, thanks to its prominent ability in coping with
variable-length signatures.
 Furthermore, it is able to handle the issue of patch extraction instability
naturally by allowing many-to-many patch correspondence.
ADVANTAGES OF PROPOSED SYSTEM:
 We further justify the proposed patch extraction approach by comparing it
with the commonly used image segmentation method, namely, Watershed.

4. Source Image
Patch 1
Patch 2
Patch n
Patch Extraction
Probabilistic
Center-
symmetric
Local
Binary
Pattern
PCSLBP
Signatures
Matching
Score
Return a
Set of
Near
Duplicate
Images
The comparisons are demonstrated, from which the advantage of the
proposedapproachis obvious.
 To describe patch visual appearance, good robustness to image orientation,
illumination and scale variations is highly desired. In our work, we propose
a patch visual appearance descriptor, viz. Probabilistic Center-symmetric
Local Binary Pattern (PCSLBP), which is an improvement of
Centersymmetric Local Binary Pattern (CSLBP).
SYSTEM ARCHITECTURE

5. SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech.
 Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
 Operating system : Windows XP/7.
 Coding Language : MATLAB
 Tool : MATLAB R2013A
REFERENCE:
Li Liu, Yue Lu, Senior Member, IEEE, and Ching Y. Suen, Fellow, IEEE,
“Variable-Length Signature for Near-Duplicate Image Matching”, IEEE
TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 4, APRIL
2015.