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In: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. 681–694

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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
Published online: 26 November 2018
DOI logo with link
https://doi.org/10.1075/z.217.21xin
Abstract
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.
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
References (40)
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. Google Scholar logo with link to Google 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 logo with link to Google Scholar
Canny, J. 1986. “A Computational Approach to Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 6: 679–698. IEEE. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google Scholar
Ma, W. Y., and Manjunath, B. S. 1999. “Netra: A Toolbox for Navigating Large Image Databases.” Multimedia Systems 7 (3): 184–198. Google Scholar logo with link to Google 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 logo with link to 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 logo with link to Google Scholar
Mehrotra, R., and Gary, J. E. 1995. “Similar-shape Retrieval in Shape Data Management.” Computer 28 (9): 57–62. Google Scholar logo with link to Google 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 logo with link to 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 logo with link to 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. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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 logo with link to Google Scholar
Town, C., and Sinclair, D. 2000. “Content-based Image Retrieval using Semantic Visual Categories.” Society of Manufacturing Engineers.Google Scholar logo with link to Google Scholar
van Rijsbergen, C. J. 1981. “Retrieval Effectiveness.” Progress in Communication Sciences 1: 91–118.Google Scholar logo with link to Google Scholar
Voorhees, E. M. 2000. “Variations in Relevance Judgments and the Measurement of Retrieval Effectiveness.” Information Processing & Management 36 (5): 697–716. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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 logo with link to 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google 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. Google Scholar logo with link to Google Scholar
Cited by (1)

Cited by one other publication

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

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