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Data Mining Techniques for Fashion Outfit Composition : A Review


Miss. Sayali Rajendra Anfat, Dr. Anup Gade
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Composing fashion outfits involves deep understanding of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., Jewelry, Bag, Pants, Dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper we provide a brief review on various data mining techniques and algorithms proposed by different authors for implementing proper fashion outfit composition.

Miss. Sayali Rajendra Anfat, Dr. Anup Gade

SVM, Genetic, Composition

  1. Z Al-Halah, R. Stiefelhagen, and K. Grauman. Fashion forward: Forecasting visual style in fashion. In ICCV, 2017.
  2. L Bossard, M. Dantone, C. Leistner, C. Wengert, T. Quack, and L. Van Gool. Apparel classification with style. In ACCV, 2012.
  3. J Carbonell and J. Goldstein. The use of mmr, diversitybased reranking for reordering documents and producing summaries. In ACM SIGIR, 1998.
  4. H Chen, A. Gallagher, and B. Girod. Describing clothing by semantic attributes. In ECCV, 2012.
  5. J Chen, J. Zhu, Z. Wang, X. Zheng, and B. Zhang. Scalable inference for logistic-normal topic models. In Advances in Neural Information Processing Systems (NIPS), 2013.
  6. Q Chen, J. Huang, R. Feris, L. M. Brown, J. Dong, and S. Yan. Deep domain adaptation for describing people based on fine-grained clothing attributes. In CVPR, 2015.
  7. J Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A Large-Scale Hierarchical Image Database. In CVPR, 2009.
  8. W Di, C. Wah, A. Bhardwaj, R. Piramuthu, and N. Sundaresan. Style finder: Fine-grained clothing style detection and retrieval. In CVPR, 2013.
  9. Q Dong, S. Gong, and X. Zhu. Multi-task curriculum transfer deep learning of clothing attributes. In WACV. IEEE, 2017.
  10. K. El-Arini, G. Veda, D. Shahaf, and C. Guestrin. Turning down the noise in the blogosphere. In ACM SIGKDD, 2009.
  11. J. Fu, J. Wang, Z. Li, M. Xu, and H. Lu. Efficient clothing retrieval with semantic-preserving visual phrases. In ACCV, 2012.
  12. B. Gong, W. Chao, K. Grauman, and F. Sha. Diverse sequential subset selection for supervised video summarization. In NIPS, 2014.
  13. C. Guestrin, A. Krause, and A. Singh. Near-optimal sensor placements in gaussian processes. In ICML, 2005.
  14. X. Han, Z. Wu, Y.-G. Jiang, and L. S. Davis. Learning fashion compatibility with bidirectional lstms. ACM MM, 2017.
  15. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
  16. R. He, C. Packer, and J. McAuley. Learning compatibility across categories for heterogeneous item recommendation. In ICDM, 2016.
  17. W.-L. Hsiao and K. Grauman. Learning the latent ‘look’: Unsupervised discovery of a style-coherent embedding from fashion images. In ICCV, 2017.
  18. Y. Hu, X. Yi, and L. Davis. Collaborative fashion recommendation: A functional tensor factorization approach. In ACM MM, 2015.

Publication Details

Published in : Volume 4 | Issue 6 | January-February - 2018
Date of Publication Print ISSN Online ISSN
2018-02-28 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
206-209 IJSRSET1848152   Technoscience Academy

Cite This Article

Miss. Sayali Rajendra Anfat, Dr. Anup Gade, "Data Mining Techniques for Fashion Outfit Composition : A Review", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 6, pp.206-209, January-February-2018.
URL : http://ijsrset.com/IJSRSET1848152.php