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“Secure Image Retrieval based on Hybrid Features and Hashes”

Ranjit R. Banshpal

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OUTLINES

• Introduction

• Scenario of work

• Methodology to be employed

• References

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.

ADMIN(secure by password

authentication)

Feature extraction( Ripplet Trns. )

Color Feature

Shape Feature

Texture FeatureCollection of images

Similarity measurement

Attribute Database

Management using Hashing

(TCH + SIFT)

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Ranking based on distance and

weightage

Extracted similar Images

Feature extraction

User Query by image

Scenario of work

Authentication using AES

MethodologySr No.

Name of Algorithm Advantages DatasetUsed

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.

REFERENCES1. 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.

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.

THANK YOU