Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Fashion image classification using matching points with linear convolution

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Social image data related to fashion is flowing through the social networks in huge amount. Analysis of this data is a challenging task due to its characteristics like voluminous, unstructured, etc. Classification provides an easy and efficient way to deal with such data. In this paper, we proposed a new approach for classification of fashion images by incorporating the concepts of linear convolution and matching points using local features. Linear convolution is used to get the representative images with important features. Then, matching points between given image and class representative images are obtained. Maximum matching points are considered while assigning a class label to the given image. Proposed approach is useful further for various applications related to fashion such as fashion recommendation, fashion trend analysis, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bhimani J, Mi N, Leeser M, Yang Z (2017) Fim: performance prediction for parallel computation in iterative data processing applications. In: Proceedings of the IEEE 10th International Conference on Cloud Computing. IEEE, Honolulu, 359–366

  2. Bhimani J, Yang Z, Leeser M, Mi N (2017) Accelerating big data applications using lightweight virtualization framework on enterprise cloud. In: Proceedings of the High Performance Extreme Computing Conference (HPEC). IEEE, Waltham, 1–7

  3. Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of the COMPSTAT', pp. 177-186

  4. Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 10(5):1055–1064

    Article  Google Scholar 

  5. Gao Y, Wang F, Luan H, Chua TS (2014) Brand data gathering from live social media streams. In: Proceedings of the ACM International Conference on Multimedia Retrieval. ACM, Glasgow, 169

  6. Gao H, Yang Z, Bhimani J, Wang T, Wang J, Sheng B, Mi N (2017) AutoPath: harnessing parallel execution paths for efficient resource allocation in multi-stage big data frameworks. IEEE, Vancouver, 1–9

  7. Hori K, Okada S, Nitta K (2016) Fashion image classification on mobile phones using layered deep convolutional neural networks. In: Proceedings of the 15 ACM International Conference on Mobile and Ubiquitous Multimedia, ACM, Rovaniemi, 359–361

  8. Inoue N, Simo-Serra E, Yamasaki T, Ishikawa H (2017) Multi-label fashion image classification with minimal human supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2261–2267

  9. Jagadeesh V, Piramuthu R, Bhardwaj A, Di W, Sundaresan N (2014) Large scale visual recommendations from street fashion images. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, 1925–1934

  10. Jegou H, Perronnin F, Douze M, Sánchez J, Perez P, Schmid C (2012) Aggregating local descriptors into a compact codes. IEEE Transaction on Pattern Analysis and Machine Intelligence 34(9):1704–1716

    Article  Google Scholar 

  11. Kim E, Fiore AM, Kim H (2013) Fashion trends: analysis and forecasting. Berg

  12. Kota Yamaguchi M, Kiapour H, Ortiz LE, Berg TL (2015) Retrieving similar styles to parse clothing. IEEE Trans Pattern Anal Mach Intell 37(5):1028–1040

    Article  Google Scholar 

  13. Li Y, Cao LL, Zhu J, Luo J (2017) Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Transactions on Multimedia

  14. Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400

  15. Liu Z, Yan S, Luo P, Wang X, Tang X (2016) Fashion landmark detection in the wild. In: Proceedings of the European Conference on Computer Vision, Springer International Publishing, 229–245

  16. Loni B, Cheung LY, Riegler M, Bozzon A, Gottlieb L, Larson M (2014) Fashion 10000: an enriched social image dataset for fashion and clothing. In proceedings of the 5th ACM Multimedia Systems Conferenc. ACM, New York, 41–46. https://doi.org/10.1145/2557642.2563675

  17. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, Springer 60(2):91–110

    Article  MathSciNet  Google Scholar 

  18. Miura S, Yamasaki T, Aizawa K (2013) SNAPPER: fashion coordinate image retrieval system. In: Proceedings of the IEEE International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, Kyoto, 784–789

  19. Nguyen HT, Almenningen T, Havig M, Schistad H, Kofod-Petersen A, Langseth H, Ramampiaro H (2014) Learning to rank for personalised fashion recommender systems via implicit feedback. Springer, Mining Intelligence and Knowledge Exploration, pp 51–61

    Google Scholar 

  20. Pawening RE, Dijaya R, Brian T, Suciati N (2015) Classification of textile image using support vector machine with textural feature. In: Proceedings of the IEEE International Conference on Information & Communication Technology and Systems (ICTS). IEEE, Surabaya, 119–122

  21. Riegler M, Larson M, Lux M, Kofler C (2014, November) How'how' reflects what's what: content-based exploitation of how users frame social images. In: Proceedings of the 22 ACM International Conference on Multimedia. IEEE, Chengdu, 397–406

  22. Rubio A, Yu LL, Simo-Serra E, Moreno-Noguer F (2017) Multi-modal embedding for main product detection in fashion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogition, 2236–2242

  23. Sofiqul Islam SM, Dey EK, Tawhid MNA, Mainul Hossain BM (2017) A CNN Based Approach for Garments Texture Design Classification. Advances in Technology Innovation 2(4):119–125

    Google Scholar 

  24. Vedaldi A, Lenc K (2015) Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM International Conference on Multimedia. ACM, Brisbane, 689–692

  25. Wang F, Qiyang Z, Baolin Y, Xu T (2016) Parsing fashion image into mid-level semantic parts based on chain-conditional random fields. IET Image Process 10(6):456–463

    Article  Google Scholar 

  26. Wang F, Qi S, Gao G, Zhao S, Wang X (2016) Logo information recognition in large-scale social media data. Multimedia Systems 22(1):63–73

    Article  Google Scholar 

  27. Wazarkar S, Keshavamurthy BN, Hussain A (2018) Probabilistic classifier for fashion image grouping using multi-layer feature extraction model. International Journal of Web Services Research, IGI Global 15(2), (In press)

  28. Wu Q, Boulanger P (2016) Enhanced Reweighted MRFs for Efficient Fashion Image Parsing. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 12(3):42

    Google Scholar 

  29. Xie X, Livermore C (2016, January) A pivot-hinged, multilayer SU-8 micro motion amplifier assembled by a self-aligned approach. In: Proceedings of the IEEE 29 International Conference on Micro Electro Mechanical Systems (MEMS). IEEE, Shanghai, 75–78

  30. Xie X, Livermore C (2017) Passively self-aligned assembly of compact barrel hinges for high-performance, out-of-plane mems actuators. In: Proceedings of the IEEE 30th International Conference on Micro Electro Mechanical Systems. IEEE, Las Vegas, 813–816

  31. Xie X, Zaitsev Y, Velásquez-García LF, Teller SJ, Livermore C (2014) Scalable, MEMS-enabled, vibrational tactile actuators for high resolution tactile displays. J Micromech Microeng 24(12):125014

    Article  Google Scholar 

  32. Xie X, Zaitsev Y, Velasquez-Garcia L, Teller S, Livermore C (2014) Compact, scalable, high-resolution, MEMS-enabled tactile displays. In: Proceedings of the Solid-State Sensors, Actuators, and Microsystems Workshop, 127–30

  33. Yamaguchi K, Berg TL, Ortiz LE (2014) Chic or social: visual popularity analysis in online fashion networks. In: Proceedings of the 22 ACM International Conference on Multimedia. ACM, Orlando, 773–776

  34. Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press. https://doi.org/10.1017/CBO9781139088510

  35. Zhang M-L, Zhou Z-H (2007) ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  MATH  Google Scholar 

  36. Zhao S, Yao H, Zhao S, Jiang X, Jiang X (2016) Multi-modal microblog classification via multi-task learning. Multimedia Tools and Applications 75(15):8921–8938

    Article  Google Scholar 

  37. Zhao S, Yao H, Gao Y, Ji R, Xie W, Jiang X, Chua TS (2016) Predicting personalized emotion perceptions of social images. In: Proceedings of the ACM Conference on Multimedia. ACM, New York, 1385–1394

  38. Zhao S, Gao Y, Ding G, Chua TS (2017) Real-time multimedia social event detection in microblog. IEEE Transactions on Cybernetics 48:625–638. https://doi.org/10.1109/TCYB.2017.2762344

  39. Zhao S, Yao H, Gao Y, Ji R, Ding G (2017) Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Transactions on Multimedia 19(3):632–645

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology Goa, Ponda, India

    Seema Wazarkar & Bettahally N. Keshavamurthy

Authors
  1. Seema Wazarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bettahally N. Keshavamurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seema Wazarkar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wazarkar, S., Keshavamurthy, B.N. Fashion image classification using matching points with linear convolution. Multimed Tools Appl 77, 25941–25958 (2018). https://doi.org/10.1007/s11042-018-5829-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5829-4

Keywords