HASHING (TCH) SIFT (Scale Invariant Feature Transform)

1. 1
“Secure Image Retrieval based on Hybrid Features and
Hashes”
Ranjit R. Banshpal
11

2. OUTLINES
• Introduction
• Scenario of work
• Methodology to be employed
• References

3. Introduction
 Scalable image search based on visual similarity has been an active
topic of research in recent years.
 Here introduces an approach that enables query-adaptive ranking of
images for avoiding identical image.

4. ADMIN
(secure by
password
authentication)
Scenario of work
Feature
extraction( Ripplet Trns. )
Color Feature
Shape Feature
Texture Feature
Collection
of images
Management using
Hashing
(TCH + SIFT)
Similarity
measurement
0
Attribute
Database
Ranking based on
distance and
weightage
Extracted similar
Images
Feature
extraction
Authentication using AES
User Query by image

5. Methodology
Sr
No.
Name of Algorithm Advantages Dataset
Used
1. HASHING (TCH)  Performance does not
decrease as the database
size increases
 Space is conserved by adding
and removing as necessary
Flickr images with
tags ,NUS WIDE
2. SIFT (Scale Invariant Feature
Transform)
Locality: features are local, so
robust to blocking and clutter
(no prior segmentation)
Distinctiveness: individual
features can be matched to a
large database of objects.
Quantity: many features can be
generated for even small objects.
Extensibility: can easily be
extended to wide range of
differing feature.

6. REFERENCES
1. Yu-Gng Jiang, Jun Wang, Member, IEEE, Xiangyang Xue, Member, IEEE, and Shih-Fu
Chang, Fellow, IEEE, "Query-Adaptive Image Search with Hash Codes”, IEEE
TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 2, FEBRUARY 2013.
2. Soo-Chang Pei, Senior Member, IEEE, and Ching-Min Cheng." Extracting Color Features
and Dynamic Matching for Image Data-Base Retrieval". IEEE ransactions On circuits and
systems for video technology, VOL. 9, NO. 3, APRIL 1999.
3. Amitava Nag, Jyoti Prakash Singh, Srabani Khan, Saswati Ghosh, Sushanta Biswas, D.
Sarkar Partha Pratim Sarkar, Image Encryption Using Affine Transform and XOR
Operation‖,International Conference on Signal Processing , Communication, Computing and
Networking Technologies (ICSCCN 2011).
4. Silpa-Anan and R. Hartley, “Optimised KD-trees for fast image descriptor matching,” in
Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
5. Bin Li, Delie Ming, Wenwen Yan, Xiao Sun, Tian Tian, and Jinwen Tian, “Image Matching
Based on Two-Column Histogram Hashing and Improved RANSAC”, IEEE GEOSCIENCE
AND REMOTE SENSING LETTERS, VOL. 11, NO. 8, AUGUST 2014.

7. 6. David Nist´er and Henrik Stew´enius, “Scalable Recognition with a Vocabulary Tree”,
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) IEEE
2006.
7. K. Mikolajczyk and J. Matas. “Improving descriptors for fast tree matching by optimal linear
projection”,. In ICCV, pages 1–8, 2007.
8. T. Liu, A. W. Moore, A. G. Gray, and K. Yang. “An investigation of practical approximate
nearest neighbor algorithms”. In NIPS, 2004.
9. H. Jegou, M. Douze, and C. Schmid, “Improving bag-of-features for Large scale image
search,” Int. J. Comput. Vision, vol. 87, pp. 191–212, 2010.