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MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction

Authors:
Kuan-Hsien Liu
Academia Sinica, Taipei, Taiwan Roc
,
Ting-Yen Chen
Academia Sinica, Taipei, Taiwan Roc
,
Chu-Song Chen
Academia Sinica, Taipei, Taiwan Roc
Authors Info & Claims
Published: 06 June 2016 Publication History

Abstract

Clothing retrieval and clothing style recognition are important and practical problems. They have drawn a lot of attention in recent years. However, the clothing photos collected in existing datasets are mostly of front- or near-front view. There are no datasets designed to study the influences of different viewing angles on clothing retrieval performance. To address view-invariant clothing retrieval problem properly, we construct a challenge clothing dataset, called Multi-View Clothing dataset. This dataset not only has four different views for each clothing item, but also provides 264 attributes for describing clothing appearance. We adopt a state-of-the-art deep learning method to present baseline results for the attribute prediction and clothing retrieval performance. We also evaluate the method on a more difficult setting, cross-view exact clothing item retrieval. Our dataset will be made publicly available for further studies towards view-invariant clothing retrieval.

References

[1]
L. Bossard, M. Dantone, C. Leistner, C. Wengert, T. Quack, and L. Van Gool. Apparel classification with style. In Computer Vision--ACCV 2012, pages 321--335. Springer, 2013.
[2]
H. Chen, A. Gallagher, and B. Girod. Describing clothing by semantic attributes. In Computer Vision--ECCV 2012, pages 609--623. Springer, 2012.
[3]
H. Chen, Z. J. Xu, Z. Q. Liu, and S. C. Zhu. Composite templates for cloth modeling and sketching. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 1, pages 943--950. IEEE, 2006.
[4]
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5315--5324, 2015.
[5]
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886--893. IEEE, 2005.
[6]
W. Di, C. Wah, A. Bhardwaj, R. Piramuthu, and N. Sundaresan. Style finder: Fine-grained clothing style detection and retrieval. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on, pages 8--13. IEEE, 2013.
[7]
B. Hasan and D. Hogg. Segmentation using deformable spatial priors with application to clothing. In BMVC, pages 1--11, 2010.
[8]
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.
[9]
I. King and T. K. Lau. A feature-based image retrieval database for the fashion, textile, and clothing industry in Hong Kong. In Proc. of International Symposium Multi-Technology Information Processing, volume 96, pages 233--240, 1996.
[10]
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012.
[11]
K. Lin, H.-F. Yang, K.-H. Liu, J.-H. Hsiao, and C.-S. Chen. Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pages 499--502. ACM, 2015.
[12]
S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, and S. Yan. Fashion parsing with weak color-category labels. Multimedia, IEEE Transactions on, 16(1):253--265, 2014.
[13]
S. Liu, J. Feng, Z. Song, T. Zhang, H. Lu, C. Xu, and S. Yan. Hi, magic closet, tell me what to wear! In Proceedings of the 20th ACM international conference on Multimedia, pages 619--628. ACM, 2012.
[14]
S. Liu, Z. Song, G. Liu, C. Xu, H. Lu, and S. Yan. Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3330--3337. IEEE, 2012.
[15]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91--110, 2004.
[16]
H. N. Ng and R. L. Grimsdale. Computer graphics techniques for modeling cloth. Computer Graphics and Applications, IEEE, 16(5):28--41, 1996.
[17]
T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971--987, 2002.
[18]
B. Siddiquie, R. S. Feris, and L. S. Davis. Image ranking and retrieval based on multi-attribute queries. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 801--808. IEEE, 2011.
[19]
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[20]
N. Wang and H. Ai. Who blocks who: Simultaneous clothing segmentation for grouping images. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 1535--1542. IEEE, 2011.
[21]
M. Yang and K. Yu. Real-time clothing recognition in surveillance videos. In Image Processing (ICIP), 18th IEEE International Conference on, pages 2937--2940. IEEE, 2011.

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    cover image ACM Conferences
    ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval
    June 2016
    452 pages
    ISBN:9781450343596
    DOI:10.1145/2911996
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    Published: 06 June 2016

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    Author Tags

    1. clothing retrieval
    2. cross-view clothing retrieval
    3. multi-view clothing
    4. view-invariant clothing retrieval

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    June 6 - 9, 2016
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    View all
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    • (2024)AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective LearningInformation10.3390/info1504019615:4(196)Online publication date: 2-Apr-2024
    • (2024)Human Image Generation: A Comprehensive SurveyACM Computing Surveys10.1145/366586956:11(1-39)Online publication date: 28-Jun-2024
    • (2024)Toward Fashion Intelligence in the Big Data Era: State-of-the-Art and Future ProspectsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.328588070:1(36-57)Online publication date: Feb-2024
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    • (2024)Clothing Type and Color Classification Using TinyML2024 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV60512.2024.10620147(1-6)Online publication date: 8-May-2024
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    • (2024)GANs and Augmented Reality in Virtual Clothing Try-On2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10467813(1-6)Online publication date: 24-Jan-2024
    • (2024)An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification2024 10th International Conference on Web Research (ICWR)10.1109/ICWR61162.2024.10533341(13-21)Online publication date: 24-Apr-2024
    • (2024)StockQM: A Cross-Frequency Dataset for Stock Prediction and a New Stock Prediction Model2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)10.1109/ICCE-Taiwan62264.2024.10674208(275-276)Online publication date: 9-Jul-2024
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    References

    References

    [1]
    L. Bossard, M. Dantone, C. Leistner, C. Wengert, T. Quack, and L. Van Gool. Apparel classification with style. In Computer Vision--ACCV 2012, pages 321--335. Springer, 2013.
    [2]
    H. Chen, A. Gallagher, and B. Girod. Describing clothing by semantic attributes. In Computer Vision--ECCV 2012, pages 609--623. Springer, 2012.
    [3]
    H. Chen, Z. J. Xu, Z. Q. Liu, and S. C. Zhu. Composite templates for cloth modeling and sketching. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 1, pages 943--950. IEEE, 2006.
    [4]
    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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5315--5324, 2015.
    [5]
    N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886--893. IEEE, 2005.
    [6]
    W. Di, C. Wah, A. Bhardwaj, R. Piramuthu, and N. Sundaresan. Style finder: Fine-grained clothing style detection and retrieval. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on, pages 8--13. IEEE, 2013.
    [7]
    B. Hasan and D. Hogg. Segmentation using deformable spatial priors with application to clothing. In BMVC, pages 1--11, 2010.
    [8]
    Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.
    [9]
    I. King and T. K. Lau. A feature-based image retrieval database for the fashion, textile, and clothing industry in Hong Kong. In Proc. of International Symposium Multi-Technology Information Processing, volume 96, pages 233--240, 1996.
    [10]
    A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012.
    [11]
    K. Lin, H.-F. Yang, K.-H. Liu, J.-H. Hsiao, and C.-S. Chen. Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pages 499--502. ACM, 2015.
    [12]
    S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, and S. Yan. Fashion parsing with weak color-category labels. Multimedia, IEEE Transactions on, 16(1):253--265, 2014.
    [13]
    S. Liu, J. Feng, Z. Song, T. Zhang, H. Lu, C. Xu, and S. Yan. Hi, magic closet, tell me what to wear! In Proceedings of the 20th ACM international conference on Multimedia, pages 619--628. ACM, 2012.
    [14]
    S. Liu, Z. Song, G. Liu, C. Xu, H. Lu, and S. Yan. Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3330--3337. IEEE, 2012.
    [15]
    D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91--110, 2004.
    [16]
    H. N. Ng and R. L. Grimsdale. Computer graphics techniques for modeling cloth. Computer Graphics and Applications, IEEE, 16(5):28--41, 1996.
    [17]
    T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971--987, 2002.
    [18]
    B. Siddiquie, R. S. Feris, and L. S. Davis. Image ranking and retrieval based on multi-attribute queries. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 801--808. IEEE, 2011.
    [19]
    K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
    [20]
    N. Wang and H. Ai. Who blocks who: Simultaneous clothing segmentation for grouping images. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 1535--1542. IEEE, 2011.
    [21]
    M. Yang and K. Yu. Real-time clothing recognition in surveillance videos. In Image Processing (ICIP), 18th IEEE International Conference on, pages 2937--2940. IEEE, 2011.