DESIGN OF COMPLICATED DUPLICATE IMAGE REPRESENTATION APPROACH BASED ON DESCRIPTOR LEARNING

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

0
Reader(s)
0
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 8 , ISSUE 2 (June 2015) > List of articles

DESIGN OF COMPLICATED DUPLICATE IMAGE REPRESENTATION APPROACH BASED ON DESCRIPTOR LEARNING

Yongjiao Wang / Xiaojie Du / Lei Liang

Keywords : hashing, image, semi supervised learning,

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 992-1,010, DOI: https://doi.org/10.21307/ijssis-2017-792

License : (CC BY-NC-ND 4.0)

Received Date : 15-July-2014 / Accepted: 30-March-2015 / Published Online: 01-June-2015

ARTICLE

ABSTRACT

In order to solve the low discrimination of image representations in complicated duplicate image detection, this paper presents a complicated duplicate image representation approach based on descriptor learning. This approach firstly formulates objective function as minimizing empirical error on the labeled data. Then the tag matrix and the classification matrix of training dataset are brought into the objective function to ensure semantic similarity. Finally, by relaxing the constraints, we can get the learning hashes. The learning hashes are used to quantify local descriptors of images into binary codes and the frequency histograms of binary codes are as image representations. Experimental results demonstrate that compared with the state-of-the-art algorithms, this approach can effectively improve the discrimination of image presentations by introducing semantic information.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] .Jegou H, Douze M, Schmid C. Hamming Embedding and Weak Geometric Consistency for
Large Scale Image Search, In Proc. of the 10th European Conference on Computer
Vision(ECCV), 2008: 304-317.
[2] San Pedro J, Siersdorfer S, Sanderson M. Content Redundancy in YouTube and its
Application to Video Tagging, ACM Transactions on Information Systems(TOIS). 2009,
29(3):13-43.
[3] G. Sengupta, T.A. Win, C. Messom, S. Demidenko and S.C. Mukhopadhyay, “Defect
analysis of grit-blasted or spray printed surface using vision sensing technique”, Proceedings
of Image and Vision Computing NZ, Nov. 26-28, 2003, Palmerston North, pp. 18-23.
[4] Kumar A, Sabharwal Y, Sen Sandeep, A simple linear time (1 + )-approximation algorithm
for k-means clustering in any dimensions, In Proc. of the 45th Annual IEEE Symposium on
Foundations of Computer Science. 2004: 454-462.
[5] Ling HF, Yan LY, Zou FH, et al. Fast Image Copy Detection Approach Based on Local
Fingerprint Defined Visual Words, Signal Processing. 2013, 93(8): 2328-2338.
[6] Wang J, Kumar S Chang SF. Semi-Supervised Hashing for Scalable Image Retrieval, In Proc.
of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2010: 3424-3431.
[7] T. Ohji, S.C. Mukhopadhyay, M. Iwahara and S. Yamada, “Performance of Repulsive Type
Magnetic Bearing System under Nonuniform Magnetization of Permanent Magnet”, IEEE
Transactions on Magnetics, Vol. 36, No. 5, pp. 3696-3698, September 2000.
[8] Lowe D. Distinctive Image Features from Scale-invariant Key points, International Journal
of Computer Vision. 2004, 60(2):91-110.
[9] Chandrasekhar V, Takacs G, Chen D, et al. CHoG: Compressed histogram of gradients a low
bit-rate feature descriptor, IEEE Conference on Computer Vision and Pattern Recognition.
2009:789-801
[10] Dong W, Wang Z, Charikar M, et al. High-Confidence Near-Duplicate Image Detection, In
Proc. of the 2th ACM International Conference on Multimedia Retrieval. 2012: 313-321.
[11] S.C. Mukhopadhyay, S. Deb Choudhury, T. Allsop, V. Kasturi and G. E. Norris, “Assessment
of pelt quality in leather making using a novel non-invasive sensing approach”, Journal of
Biochemical and Biophysical methods, Elsevier, JBBM Vol. 70, pp. 809-815, 2008.
[12] Xie HT, Gao K, Zhang YD, et al. Pairwise Weak Geometric consistency for large scale image
search[C].In Proc. of the 1th ACM International Conference on Multimedia Retrieval, Trento,
Italy, 2011:186-195
[13] Liu, Z. ; Li, H. ; Zhou, W. ; Hong, R.; Tian, Q,Uniting Keypoints: Local Visual Information
Fusion for Large Scale Image Search, IEEE Transactions on Multimedia, vol.pp,no.99, pp.1-
6, 2015.
[14] S.C. Mukhopadhyay, F.P. Dawson, M. Iwahara and S. Yamada, “A Novel Compact Magnetic
Current Limiter for Three Phase Applications”, IEEE Transactions on Magnetics, Vol. 36, No.
5, pp. 3568-3570, September 2000.
[15] Kristen Grauman, Rob Fergus, Learning Binary Hash Codes for Large-Scale Image Search,
Studies in Computational Intelligence, Vol.411, 2013, pp 49-87.
[16] Zhen Liu, Houqiang Li, Wengang Zhou, Ruizhen Zhao, Qi Tian, Contextual Hashing for
Large-Scale Image Search, IEEE Transactions on Image Processing, vol. 23 , no.4, pp.1606 -
1614, 2014.
[17] Swati Agrawal, Ashish Sureka,Copyright Infringement Detection of Music Videos on
YouTube by Mining Video and Uploader Meta-data, Lecture Notes in Computer Science, vol.
8302, 2013, pp 48-67.
[18] Ulges, A. ; Koch, M. ; Borth, D. ; Breuel, T.M,TubeTagger - YouTube-based Concept
Detection, IEEE International Conference on Data Mining Workshops, pp.190 - 195, 2009.
[19] Mohammed El Agha, et al., Efficient and Fast Initialization Algorithm for K-means
Clustering, I.J. Intelligent Systems and Applications, 2012, 1, 21-31.
[20] S. Yamada, K. Chomsuwan, S.C. Mukhopadhyay, M. Iwahara, M. Kakikawa and I. Nagano,
“Detection of Magnetic Fluid Volume Density with a GMR Sensor”, Journal of Magnetics
Society of Japan, Vol. 31, No. 2, pp. 44-47, 2007.
[21] Leongwai Yie, Joel Than Chia Ming, Features of Sleep Apnea Recognition and Analysis,
International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 2, pp, 481 – 497,
2014.
[22] Wenqing Che and Wang Tao, A Scene Recognition Algorithm Based on Multi-instance
Learning, International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 2,
pp,1470 – 1492, 2014.

EXTRA FILES

COMMENTS