Abstract
The fashion domain has been one of the most growing areas of e-commerce, hence the issue of facilitating cloth searching in fashionrelated websites becomes an important topic of research. The paper deals with searching for similar outfits in the clothing images database, using information extracted from unconstrained images containing human silhouettes. Medoids-based clustering is introduced in order to detect groups of similar outfits and speed up the retrieval procedure. Exemplary results of experiments performed on real clothing datasets are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brookings, T., Grashow, R., Marder, E.: Statistics of neuronal identification with open and closed loop measures of intrinsic excitability. Frontiers in Neural Circuits 6(19) (2012)
Chen, H., Gallagher, A., Girod, B.: Describing clothing by semantic attributes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 609–623. Springer, Heidelberg (2012)
Chen, Q., Li, J., Liu, Z., Lu, G., Bi, X., Wang, B.: Measuring clothing image similarity with bundled features. International Journal of Clothing Science and Technology 25(2), 119–130 (2013)
Di, W., Wah, C., Bhardwaj, A., Piramuthu, R., Sundaresan, N.: Style finder: Fine-grained clothing style detection and retrieval. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 8–13. IEEE (2013)
Eichner, M., Ferrari, V.: Better appearance models for pictorial structures (2009)
Eichner, M., Marin-Jimenez, M., Zisserman, A., Ferrari, V.: 2d articulated human pose estimation and retrieval in (almost) unconstrained still images. International Journal of Computer Vision 99(2), 190–214 (2012)
Forczmański, P., Frejlichowski, D., Czapiewski, P., Okarma, K., Hofman, R.: Comparing clothing styles by means of computer vision methods. In: Proceedings of International Conference on Computer Vision and Graphics, ICCVG 2014 (2014)
Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. North-Holland (1987)
Liu, Z., Wang, J., Chen, Q., Lu, G.: Clothing similarity computation based on tlac. International Journal of Clothing Science and Technology 24(4), 273–286 (2012)
Ramanan, D.: Part-based models for finding people and estimating their pose. In: Visual Analysis of Humans, pp. 199–223. Springer (2011)
Ramanan, D., Forsyth, D.A., Zisserman, A.: Tracking people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 65–81 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. 511–518. IEEE (2001)
Yamada, A., Pickering, M., Jeannin, S., Jens, L.: Mpeg-7 visual part of experimentation model version 9.0-part 3 dominant color, iso. Technical report, IEC JTC1/SC29/WG11 (2001)
Zhang, W., Begole, B., Chu, M., Liu, J., Yee, N.: Real-time clothes comparison based on multi-view vision. In: Second ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, pp. 1–10. IEEE (2008)
Zisserman, A.: Human pose estimation in images and videos (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Czapiewski, P., Forczmański, P., Frejlichowski, D., Hofman, R. (2015). Clustering-Based Retrieval of Similar Outfits Based on Clothes Visual Characteristics. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-10662-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10661-8
Online ISBN: 978-3-319-10662-5
eBook Packages: EngineeringEngineering (R0)