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RingFIR: A Large Volume Earring Dataset for Fashion Image Retrieval

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Computer Vision and Image Processing (CVIP 2020)

Abstract

Fashion image retrieval (FIR) is a challenging task, which involves similar item searching from a massive collection of fashion products based on a query image. FIR in different garments and shoes are popular in literature. More complex fashion products such as ornaments are getting less attention. Here, we introduce a new earring dataset, namely, RingFIR. The dataset is a collection of (\(\sim \)2.6K) high-quality images collected from major India based jewellery chains. The dataset is labelled in 46 classes in a structured manner. We have benchmarked the dataset using state-of-the-art image retrieval methods. We believe that the dataset is challenging and will attract computer vision researchers in the future. The dataset is available publicly (https://github.com/skarifahmed/RingFIR).

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References

  1. Long, F., Zhang, H., Feng, D.D.: Fundamentals of content-based image retrieval. In: Feng, D.D., Siu, W.C., Zhang, H.J. (eds.) Multimedia Information Retrieval and Management. Signals and Communication Technology, pp. 1–26. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-662-05300-3_1

  2. Yasmin, M., Mohsin, S., Sharif, M.: Intelligent image retrieval techniques: a survey. J. Appl. Res. Technol. 12(1), 87–103 (2014)

    Article  Google Scholar 

  3. Lehmann, T.M., et al.: Content-based image retrieval in medical applications. Methods Inf. Med. 43(04), 354–361 (2004)

    Article  Google Scholar 

  4. Sharma, P., Reilly, R.B.: A colour face image database for benchmarking of automatic face detection algorithms. In: Proceedings EC-VIP-MC 2003. 4th EURASIP Conference Focused on Video/Image Processing and Multimedia Communications (IEEE Cat. No. 03EX667), vol. 1, pp. 423–428. IEEE (2003)

    Google Scholar 

  5. da Silva Torres, R., Falcao, A.X.: Content-based image retrieval: theory and applications. RITA 13(2), 161–185 (2006)

    Google Scholar 

  6. Choraś, R.S.: Cbir system for detecting and blocking adult images. In: Proceedings of the 9th WSEAS International Conference on Signal Processing, pp. 52–57 (2010)

    Google Scholar 

  7. Corbiere, C., Ben-Younes, H., Ramé, A., Ollion, C.: Leveraging weakly annotated data for fashion image retrieval and label prediction. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2268–2274 (2017)

    Google Scholar 

  8. Hadi Kiapour, M., Han, X., Lazebnik, S., Berg, A.C., Berg, T.L.: Where to buy it: matching street clothing photos in online shops. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3343–3351 (2015)

    Google Scholar 

  9. Huang, J., Feris, R.S., Chen, Q., Yan, S.: Cross-domain image retrieval with a dual attribute-aware ranking network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1062–1070 (2015)

    Google Scholar 

  10. Bossard, L., Dantone, M., Leistner, C., Wengert, C., Quack, T., Van Gool, L.: Apparel classification with style. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part IV. LNCS, vol. 7727, pp. 321–335. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37447-0_25

    Chapter  Google Scholar 

  11. Dong, Q., Gong, S., Zhu, X.: Multi-task curriculum transfer deep learning of clothing attributes. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 520–529. IEEE (2017)

    Google Scholar 

  12. Kalantidis, Y., Kennedy, L., Li, L.J.: Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos. In: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, pp. 105–112 (2013)

    Google Scholar 

  13. Hu, Y., Yi, X., Davis, L.S.: Collaborative fashion recommendation: a functional tensor factorization approach. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 129–138 (2015)

    Google Scholar 

  14. Li, Y., Cao, L., Zhu, J., Luo, J.: Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Trans. Multimedia 19(8), 1946–1955 (2017)

    Article  Google Scholar 

  15. Liu, S., et al.: Hi, magic closet, tell me what to wear! In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 619–628 (2012)

    Google Scholar 

  16. Park, S., Shin, M., Ham, S., Choe, S., Kang, Y.: Study on fashion image retrieval methods for efficient fashion visual search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  17. Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1096–1104 (2016)

    Google Scholar 

  18. Ge, Y., Zhang, R., Wang, X., Tang, X., Luo, P.: DeepFashion2: a versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. arXiv preprint arXiv:1901.07973 (2019)

  19. Zhou, W., et al.: Fashion recommendations through cross-media information retrieval. J. Vis. Commun. Image Represent. 61, 112–120 (2019)

    Article  Google Scholar 

  20. Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)

  21. Loni, B., Cheung, L.Y., Riegler, M., Bozzon, A., Gottlieb, L., Larson, M.: Fashion 10000: an enriched social image dataset for fashion and clothing. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 41–46 (2014)

    Google Scholar 

  22. Zheng, S., Yang, F., Kiapour, M.H., Piramuthu, R.: ModaNet: a large-scale street fashion dataset with polygon annotations. In: ACM Multimedia Conference on Multimedia Conference, pp. 1670–1678. ACM (2018)

    Google Scholar 

  23. Chen, Q., Huang, J., Feris, R., Brown, L.M., Dong, J., Yan, S.: Deep domain adaptation for describing people based on fine-grained clothing attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5315–5324 (2015)

    Google Scholar 

  24. Vasileva, M.I., Plummer, B.A., Dusad, K., Rajpal, S., Kumar, R., Forsyth, D.: Learning type-aware embeddings for fashion compatibility. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part XVI. LNCS, vol. 11220, pp. 405–421. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_24

    Chapter  Google Scholar 

  25. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)

    Google Scholar 

  26. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  27. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)

    Google Scholar 

  28. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44

    Chapter  Google Scholar 

  29. Karu, K., Jain, A.K.: Fingerprint classification. Pattern Recogn. 29(3), 389–404 (1996)

    Article  Google Scholar 

  30. Garcia, M.B., Revano, T.F., Habal, B.G.M., Contreras, J.O., Enriquez, J.B.R.: A pornographic image and video filtering application using optimized nudity recognition and detection algorithm. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–5. IEEE (2018)

    Google Scholar 

  31. Xinchen Liu, W., Liu, T.M., Ma, H.: PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimedia 20(3), 645–658 (2017)

    Google Scholar 

  32. Han, X., Wu, Z., Jiang, Y.G., Davis, L.S.: Learning fashion compatibility with bidirectional LSTMs. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1078–1086 (2017)

    Google Scholar 

  33. Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)

    Google Scholar 

  34. Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: MNIST: extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921–2926. IEEE (2017)

    Google Scholar 

  35. Grgic, M., Delac, K., Grgic, S.: SCface-surveillance cameras face database. Multimedia Tools Appl. 51(3), 863–879 (2011)

    Article  Google Scholar 

  36. Lou, Y., Bai, Y., Liu, J., Wang, S., Duan, L.: VERI-wild: a large dataset and a new method for vehicle re-identification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3235–3243 (2019)

    Google Scholar 

  37. Monroe, M.E., Tolić, N., Jaitly, N., Shaw, J.L., Adkins, J.N., Smith, R.D.: Viper: an advanced software package to support high-throughput LC-MS peptide identification. Bioinformatics 23(15), 2021–2023 (2007)

    Article  Google Scholar 

  38. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)

    Google Scholar 

  39. Kinli, F., Ozcan, B., Kiraç, F.: Fashion image retrieval with capsule networks. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  40. Lin, K., Yang, H.F., Liu, K.H., Hsiao, J.H., Chen, C.S.: Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: ACM International Conference on Multimedia Retrieval, pp. 499–502. ACM (2015)

    Google Scholar 

  41. Zhao, B., Feng, J., Wu, X., Yan, S.: Memory-augmented attribute manipulation networks for interactive fashion search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1520–1528 (2017)

    Google Scholar 

  42. Jetchev, N., Bergmann, U.: The conditional analogy GAN: swapping fashion articles on people images. In: IEEE International Conference on Computer Vision, pp. 2287–2292 (2017)

    Google Scholar 

  43. Wang, W., Xu, Y., Shen, J., Zhu, S.C.: Attentive fashion grammar network for fashion landmark detection and clothing category classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4271–4280 (2018)

    Google Scholar 

  44. Erkut, U., Bostancıoğlu, F., Erten, M., Özbayoğlu, A.M., Solak, E.: HSV color histogram based image retrieval with background elimination. In: 2019 1st International Informatics and Software Engineering Conference (UBMYK), pp. 1–5. IEEE (2019)

    Google Scholar 

  45. Liao, Q.: Comparison of several color histogram based retrieval algorithms. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1670–1673. IEEE (2016)

    Google Scholar 

  46. Ha, I., Kim, H., Park, S., Kim, H.: Image retrieval using BIM and features from pretrained VGG network for indoor localization. Build. Environ. 140, 23–31 (2018)

    Article  Google Scholar 

  47. Pelka, O., Nensa, F., Friedrich, C.M.: Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. PLoS One 13(11), e0206229 (2018)

    Article  Google Scholar 

  48. Zhang, J., Chaoquan, L., Li, X., Kim, H.-J., Wang, J.: A full convolutional network based on DenseNet for remote sensing scene classification. Math. Biosci. Eng 16(5), 3345–3367 (2019)

    Article  Google Scholar 

  49. Saxen, F., Werner, P., Handrich, S., Othman, E., Dinges, L., Al-Hamadi, A.: Face attribute detection with mobilenetv2 and NasNet-mobile. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 176–180. IEEE (2019)

    Google Scholar 

  50. Ilhan, H.O., Sigirci, I.O., Serbes, G., Aydin, N.: A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods. Med. Biol. Eng. Comput. 58(5), 1047–1068 (2020). https://doi.org/10.1007/s11517-019-02101-y

    Article  Google Scholar 

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Islam, S.M., Joardar, S., Sekh, A.A. (2021). RingFIR: A Large Volume Earring Dataset for Fashion Image Retrieval. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_9

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  • DOI: https://doi.org/10.1007/978-981-16-1092-9_9

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