Cover not available
Part of
Progress in Colour Studies: Cognition, language and beyond
Edited by Lindsay W. MacDonald, Carole P. Biggam and Galina V. Paramei
[Not in series 217] 2018
► pp. 391–404

Chapter 21
An empirical study on fabric image retrieval with multispectral images using colour and pattern features

| The Hong Kong Polytechnic University, Hong Kong
| The Hong Kong Polytechnic University, Hong Kong
| The Hong Kong Polytechnic University, Hong Kong
| The Hong Kong Polytechnic University, Hong Kong

This article presents technical details of a fabric image retrieval system using multispectral images. The goal of the system is to retrieve similar fabric images effectively and efficiently when given a query image. The web-based retrieval system can be accessed through web browsers. The database contains 2,100 fabric images of both yarn-dyed and printed fabrics with a high variety of colours and patterns. The images were captured using the 16-channel multispectral Imaging Colour Measurement (ICM) System for ensuring colour accuracy. Six retrieval models are investigated and compared. Three of the models use colour features while the other three use local pattern features. Future research directions in fabric image retrieval are proposed.

Keywords: fabric image, image retrieval, multispectral image, fabric features, colour measurement
Article outline
  • 1.Introduction
  • 2.The multispectral Imaging Colour Measurement (ICM) system
  • 3.Retrieval models
    • 3.1Colour-based retrieval models
      • 3.1.1Basic statistical model
      • 3.1.2MPEG-7 Dominant Colour Descriptor model
      • 3.1.3Pantone colour model
    • 3.2Pattern-based retrieval models
  • 4.Experiments
  • 5.Conclusion and future work
    • 5.1Region segmentation
    • 5.2Deep learning
  • References
Published online: 26 November 2018
DOI logo
https://doi.org/10.1075/z.217.21xin
References
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L.
2008 “Speeded-up Robust Features (SURF).” Computer Vision and Image Understanding, 110 (3): 346–359. DOI logoGoogle Scholar
Bengio, Y., Courville, A. C., and Vincent, P.
2012 “Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives.” CoRR, abs/1206.5538, 1.Google Scholar
Canny, J.
1986 “A Computational Approach to Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 6: 679–698. IEEE. DOI logoGoogle Scholar
Chapelle, O., Metlzer, D., Zhang, Y., and Grinspan, P.
2009 “Expected Reciprocal Rank for Graded Relevance.” Proceedings of 18th ACM Conference on Information and Knowledge Management, 621–630. New York: ACM.Google Scholar
Dahl, G. E., Yu, D., Deng, L., and Acero, A.
2012 “Context-dependent Pre-trained Deep Neural Networks for Large-vocabulary Speech Recognition.” IEEE Transactions on Audio, Speech, and Language Processing 20 (1): 30–42. DOI logoGoogle Scholar
Han, Y., Zheng, D., Baciu, G., Feng, X., and Li, M.
2013 “Fuzzy Region Competition-based Auto-colour-theme Design for Textile Images.” Textile Research Journal 83 (6): 638–650. DOI logoGoogle Scholar
Han, Y., Xu, C., Baciu, G., and Li, M.
2015 “Lightness Biased Cartoon-and-texture Decomposition for Textile Image Segmentation.” Neurocomputing 168: 575–587. DOI logoGoogle Scholar
Huang, X., Chen, D., Han, X. H., and Chen, Y. W.
2013 “Global and Local Features for Accurate Impression Estimation of Cloth Fabric Images.” IEEE/SICE International Symposium on System Integration (SII), 486–489. IEEE. DOI logoGoogle Scholar
Jing, F., Li, M., Zhang, L., Zhang, H. J., and Zhang, B.
2003 “Learning in Region-based Image Retrieval.” Image and Video Retrieval, 199–204.Google Scholar
Jing, J.; Li, Q.; Li, P.; Zhang, H.; and Zhang, L.
2015 “Patterned Fabric Image Retrieval Using Colour and Space Features.” Journal of Fiber Bioengineering and Informatics, 8 (3): 603–614. DOI logoGoogle Scholar
Krizhevsky, A., Sutskever, I., and Hinton, G. E.
2012 “Imagenet Classification with Deep Convolutional Neural Networks.” Proceedings of 25th International Conference on Neural Information Processing Systems. Vol. 1: 1097–1105. ACM.Google Scholar
Liu, Y., Zhang, D., Lu, G., and Ma, W. Y.
2007 “A Survey of Content-based Image Retrieval with High-level Semantics.” Pattern Recognition 40 (1): 262–282. DOI logoGoogle Scholar
Lowe, D. G.
1999 “Object Recognition from Local Scale-invariant Features.” Proceedings of Seventh IEEE International Conference on Computer Vision. Vol. 2: 1150–1157. IEEE. DOI logoGoogle Scholar
Luo, L.
2015 An Investigation of Colour Measurement of Yarn-dyed Fabrics based on the Multispectral Imaging System. Doctoral Dissertation. Hong Kong Polytechnic University.Google Scholar
Luo, L., Shao, S. J., Shen, H. L., and Xin, J. H.
2013 “An Unsupervised Method for Dominant Colour Region Segmentation in Yarn-dyed Fabrics.” Colouration Technology 129 (6): 389–397. DOI logoGoogle Scholar
Luo, L., Shen, H. L., Shao, S. J., and Xin, J. H.
2015 “An Efficient Method for Solid-colour and Multicolour Region Segmentation in Real Yarn-dyed Fabric Images.” Colouration Technology 131 (2): 120–130. DOI logoGoogle Scholar
Ma, W. Y., and Manjunath, B. S.
1999 “Netra: A Toolbox for Navigating Large Image Databases.” Multimedia Systems 7 (3): 184–198. DOI logoGoogle Scholar
MacQueen, J.
1967 “Some Methods for Classification and Analysis of Multivariate Observations.” Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. 1 (14): 281–297.Google Scholar
Manjunath, B. S., Salembier, P., and Sikora, T.
2002 Introduction to MPEG-7: Multimedia Content Description Interface. Vol. 1. New York: John Wiley & Sons.Google Scholar
Mehrotra, R., and Gary, J. E.
1995 “Similar-shape Retrieval in Shape Data Management.” Computer 28 (9): 57–62. DOI logoGoogle Scholar
Mikolov, T., Yih, W. T., and Zweig, G.
2013 “Linguistic Regularities in Continuous Space Word Representations.” In hlt-Naacl 13: 746–751.Google Scholar
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G.
2011 “ORB: An Efficient Alternative to SIFT or SURF.” IEEE International Conference on Computer Vision (ICCV), 2564–2571. IEEE.Google Scholar
Rui, Y., Huang, T. S., and Chang, S. F.
1999 “Image Retrieval: Current Techniques, Promising Directions, and Open Issues.” Journal of Visual Communication and Image Representation 10 (1): 39–62. DOI logoGoogle Scholar
Sethi, I. K., Coman, I. L., and Stan, D.
2001 “Mining Association Rules between Low-level Image Features and High-level Concepts.” In Data Mining and Knowledge Discovery: Theory, Tools, and Technology III. Proc. SPIE. 4384: 279–290.Google Scholar
Sharma, G., Wu, W., and Dalal, E. N.
2005 “The CIEDE2000 Colour‐difference Formula: Implementation Notes, Supplementary Test Data, and Mathematical Observations.” Color Research & Application 30 (1): 21–30. DOI logoGoogle Scholar
Shen, H. L., Cai, P. Q., Shao, S. J., and Xin, J. H.
2007 “Reflectance Reconstruction for Multispectral Imaging by Adaptive Wiener Estimation.” Optics Express 15 (23): 15545–15554. DOI logoGoogle Scholar
Shen, H. L., Zheng, Z. H., Wang, W., Du, X., Shao, S. J., and Xin, J. H.
2012 “Autofocus for Multispectral Camera using Focus Symmetry.” Applied Optics 51 (14): 2616–2623. DOI logoGoogle Scholar
Sivic, J., and Zisserman, A.
2009 “Efficient Visual Search of Videos cast as Text Retrieval.” IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (4): 591–606. DOI logoGoogle Scholar
Tong, S., and Chang, E.
2001 “Support Vector Machine Active Learning for Image Retrieval.” Proceedings of 9th ACM international Conference on Multimedia, 107–118. ACM.Google Scholar
Town, C., and Sinclair, D.
2000 “Content-based Image Retrieval using Semantic Visual Categories.” Society of Manufacturing Engineers.Google Scholar
van Rijsbergen, C. J.
1981 “Retrieval Effectiveness.” Progress in Communication Sciences 1: 91–118.Google Scholar
Voorhees, E. M.
2000 “Variations in Relevance Judgments and the Measurement of Retrieval Effectiveness.” Information Processing & Management 36 (5): 697–716. DOI logoGoogle Scholar
Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., and Li, J.
2014 “Deep Learning for Content-based Image Retrieval: A Comprehensive Study.” Proceedings of 22nd ACM international Conference on Multimedia, 157–166. ACM.Google Scholar
Wang, J. Z., Li, J., and Wiederhold, G.
2001 “SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries.” IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (9): 947–963. DOI logoGoogle Scholar
Wong, K. M., and Po, L. M.
2004 “MPEG-7 Dominant Color Descriptor-based Relevance Feedback using Merged Palette Histogram.” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. (ICASSP’04). Vol. 3. IEEE.Google Scholar
Xu, L., Yan, Q., Xia, Y., and Jia, J.
2012 “Structure Extraction from Texture via Relative Total Variation.” ACM Transactions on Graphics (TOG) 31 (6): 139. DOI logoGoogle Scholar
Yu, D., Seltzer, M. L., Li, J., Huang, J. T., and Seide, F.
2013 “Feature Learning in Deep Neural Networks-studies on Speech Recognition Tasks.” arXiv preprint:1301.3605.Google Scholar
Zhang, J., Pan, R., Gao, W., and Zhu, D.
2015 “Automatic Detection of Layout of Colour Yarns of Yarn-dyed Fabric. Part 1: Single-system‐mélange Colour Fabrics.” Color Research & Application 40 (6): 626–636. DOI logoGoogle Scholar
Zheng, D., Baciu, G., and Hu, J.
2009 “Accurate Indexing and Classification for Fabric Weave Patterns using Entropy-based Approach.” Proceedings of 8th IEEE International Conference on Cognitive Informatics. ICCI’09, 357–364. IEEE. DOI logoGoogle Scholar
Zheng, X., Cai, D., He, X., Ma, W. Y., and Lin, X.
2004 “Locality Preserving Clustering for Image Database.” Proceedings of 12th ACM International Conference on Multimedia, 885–891. ACM. DOI logoGoogle Scholar
Cited by

Cited by 1 other publications

Jing, Junfeng & Huanhuan Ren
2021. Defect Detection of Printed Fabric Based on RGBAAM and Image Pyramid. Autex Research Journal 21:2 ► pp. 135 ff. DOI logo

This list is based on CrossRef data as of 22 april 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.