Open Access   Article Go Back

Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey

Nikhil. S. Tengli1 , Suvarna Nandyal2

Section:Survey Paper, Product Type: Journal Paper
Volume-07 , Issue-14 , Page no. 88-92, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si14.8892

Online published on May 15, 2019

Copyright © Nikhil. S. Tengli, Suvarna Nandyal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Nikhil. S. Tengli, Suvarna Nandyal, “Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.88-92, 2019.

MLA Style Citation: Nikhil. S. Tengli, Suvarna Nandyal "Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey." International Journal of Computer Sciences and Engineering 07.14 (2019): 88-92.

APA Style Citation: Nikhil. S. Tengli, Suvarna Nandyal, (2019). Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey. International Journal of Computer Sciences and Engineering, 07(14), 88-92.

BibTex Style Citation:
@article{Tengli_2019,
author = {Nikhil. S. Tengli, Suvarna Nandyal},
title = {Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {14},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {88-92},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1096},
doi = {https://doi.org/10.26438/ijcse/v7i14.8892}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i14.8892}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1096
TI - Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey
T2 - International Journal of Computer Sciences and Engineering
AU - Nikhil. S. Tengli, Suvarna Nandyal
PY - 2019
DA - 2019/05/15
PB - IJCSE, Indore, INDIA
SP - 88-92
IS - 14
VL - 07
SN - 2347-2693
ER -

           

Abstract

In the recent years, the use of e-commerce based applications via Internet has grown rapidly, thus increasing the volume of data in the web. Therefore it necessary to have faster retrieval of required data from the web. This paper provides a comprehensive review of various image retrieval techniques with their problems. The survey presents various techniques used so far for the Image Retrieval from the Web based applications, in order to make more efficient way of retrieving the information by using image retrieval techniques. The survey describes which techniques are used for image retrieval and the problem faced during the retrieval process. Finally, based on the use of existing techniques and the demand from the real-time applications a shopping guide will be presented with enhanced features of image retrieval techniques named as Click-n-Purchase, where the input for this application is taken from the mobiles and the visual search of the related images can be extracted from web based fashion domain based applications, so that user can be able to search their favourite items in less amount of time.

Key-Words / Index Term

Image Retrieval Techniques, Mobile Visual Search, Fashion domain, Click-n-Purchase

References

[1] Mehmood, Zahid and Abbas, Fakhar and Mahmood, Toqeer and Javid, Muhammad Arshad and Rehman, Amjad and Nawaz, Tabassam, Content-Based Image Retrieval Based on Visual Words Fusion Versus Features Fusion of Local and Global Features, Arabian Journal for Science and Engineering, 2018, pp. 1-20.
[2] Katrien Laenen, Susana Zoghbi, and Marie-Francine Moens, Web Search of Fashion Items with Multimodal Querying, Eleventh ACM International Conference on Web Search and Data Mining (WSDM `18), 2018, pp. 342-350.
[3] Angelo Nodari, Matteo Ghiringhelli, Alessandro Zamberletti, Marco Vanetti, Simone Albertini, Ignazio Gallo, “A mobile visual search application for content based image retrieval in the fashion domain”, 10th International Workshop on Content-Based Multimedia Indexing, 2012.
[4] J. Cychnerski, A. Brzeski, A. Boguszewski, M. Marmolowski and M. Trojanowicz, "Clothes detection and classification using convolutional neural networks," 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, 2017, pp. 1-8. doi: 10.1109/ETFA.2017.8247638
[5] Zhou, Dibin & Hu, Baokun & Wang, Qihui & Hu, Bin & Jia, Leiping & Wu, Yingfei & Xie, Lijun. “Design of Shopping Guide System with Image Retrieval Based on Mobile Platform”. 10.2991/3ca-13.2013.37, 2013.
[6] Liu Shuguang, Qu Pingge “Fabric Texture Classification Based on Wavelet Packet”, The Eighth International Conference on Electronic Measurement and Instruments,2017.
[7] Tom Yeh1, Kristen Grauman1, Konrad Tollmar2, Trevor Darrell, “A Picture is Worth a Thousand Keywords: Image-Based Object Search on a Mobile Platform”, CHI 2005, April 2-7, 2005. Portland, Oregon, USA.
[8] Yixin Chen, Member, IEEE, Jinbo Bi, Member, IEEE, and James Z. Wang, Senior Member, IEEE, “MILES: Multiple-Instance Learning”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 28, NO. 12, DECEMBER 2006
[9] P. F. Li, J. Wang, H. H. Zhang and J. F. Jing, "Automatic woven fabric classification based on support vector machine," International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), Xiamen, 2012, pp. 581-584.
[10] Min, Weiqing and Jiang, Shuqiang and Wang, Shuhui and Xu, Ruihan and Cao, Yushan and Herranz, Luis and He, Zhiqiang,”A survey on context-aware mobile visual recognition, Multimedia Systems, 23(6), 2017, pp. 647-665.
[11] Weiqing Min, Shuqiang Jiang, Shuhui Wang, Ruihan Xu, Yushan Cao, Luis Herranz, and Zhiqiang He, “A survey on context-aware mobile visual recognition”. Multimedia Systems, 2017, pp. 647-665.
[12] Mitul Kumar Ahirwal, Anil Kumar, and Girish Kumar Singh, “An Approach to Design Self Assisted CBIR System”, International Conference on Graphics and Signal Processing (ICGSP`17),pp. 21-25.
[13] Xin Ji, Wei Wang, Meihui Zhang, and Yang Yang, “Cross-Domain Image Retrieval with Attention Modelling”, ACM on Multimedia Conference(MM`17),2017,pp.1654-1662.
[14] C. Huang, S. Zhang, X. Lin, X. Liu and R. Ji, "Deep-based fisher vector for mobile visual search“, IEEE International Conference on Image Processing (ICIP), 2017, pp. 3430-3434.
[15] Y. H. Kuo and W. H. Hsu, "Dehashing: Server-Side Context-Aware Feature Reconstruction for Mobile Visual Search“, IEEE Transactions on Circuits and Systems for Video Technology, 27(1), 2017, pp. 139-148.
[16] A. Rahman, E. Winarko and M. E. Wibowo, "Mobile content based image retrieval architectures,“ 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp.1-4.
[17] C. Corbière, H. Ben-Younes, A. Ramé and C. Ollion, "Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction,“ IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 2268-2274.
[18] J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” in IEEE Conference on Computer Vision and Pattern Recognition, 2003, pp. 1470–1477.
[19] J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman, Object retrieval with large vocabularies and fast spatial matching, 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, 2007, pp. 1-8.
[20] W. Zhou, Y. Lu, H. Li, Y. Song, and Q. Tian, “Spatial coding for large scale partial-duplicate web image search,” in ACM International Conference on Multimedia, 2010, pp. 511–520.
[21] O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman, “Total recall: Automatic query expansion with a generative feature model for object retrieval,” in International Conference on Computer Vision, 2007, pp. 1–8.
[22] D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 2161–2168.
[23] Z. Wu, Q. Ke, M. Isard, and J. Sun, “Bundling features for large scale partial-duplicate web image search,” in IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 25–32.
[24] X. Wang, M. Yang, T. Cour, S. Zhu, K. Yu, and T. X. Han, “Contextual weighting for vocabulary tree based image retrieval,” in International Conference on Computer Vision, 2011, pp. 209–216.
[25] L. Zheng, S. Wang, and Q. Tian, “Coupled binary embedding for large-scale image retrieval,” IEEE Transactions on Image Processing (TIP), vol. 23, no. 8, pp. 3368–3380, 2014.
[26] Y. Cao, C. Wang, L. Zhang, and L. Zhang, “Edgel index for largescale sketch-based image search,” in IEEE Conference on C Vision and Pattern Recognition (CVPR), 2011, pp. 761–768.
[27] J.-P. Heo, Y. Lee, J. He, S.-F. Chang, and S.-E. Yoon, “Spherical hashing,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012, pp. 2957–2964.
[28] J. Tang, Z. Li, M. Wang, and R. Zhao, “Neighborhood discriminant hashing for large-scale image retrieval,” IEEE Transactions on Image Processing (TPI), vol. 24, no. 9, pp. 2827–2840, 2015.
[29] L. Wu, K. Zhao, H. Lu, Z. Wei, and B. Lu, “Distance preserving marginal hashing for image retrieval,” in IEEE International Conference on Multimedia and Expo (ICME), 2015, pp. 1–6.
[30] K. Jiang, Q. Que, and B. Kulis, “Revisiting kernelized localitysensitive hashing for improved large-scale image retrieval,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4933–4941.