Due to the similarities between cashmere and wool, the automatic identification of these two animal fibers continues to be a huge challenge in textile society. In this paper, for the identification of micrographs of cashmere and wool, bag-of-words and spatial pyramid matching are used. Each fiber image was regarded as a collection of feature vectors in our logic. The vectors, extracted from the original dataset, were fed into a support vector machine for supervised classification. The codebook size and the resolution level were completely investigated. The experimental results indicated that the image segmentation delivered a positive contribution in enhancing the accuracy of classification. The overall performance of the model was robust under various blend ratios. It verifies that the bag-of-words with spatial pyramid match is an effective approach to the identification of cashmere and wool fibers.
ZoccolaMLuNMossottiRet al.Identification of wool, cashmere, yak, and angora rabbit fibers and quantitative determination of wool and cashmere in blend: a near infrared spectroscopy study. Fiber Polym2013; 14: 1283–1289.
2.
Zhong Y, Lu K, Tian J, et al. Wool/cashmere identification based on projection curves. Text Res J. 2017; 87: 1730–1741. DOI: 10.1177/0040517516658516.
3.
TridicoSRNatural animal textile fibers: structure, characteristics and identification. In: HouckMM (ed). Identification of Textile Fibers, Cambridge, England: Woodhead Publishing Ltd, 2009, pp. 27–87.
4.
VineisCTonettiCPaolellaSet al.A UPLC/ESI–MS method for identifying wool, cashmere and yak fibres. Text Res J2014; 84: 953–958.
5.
TangMZhangWZhouHet al.A real-time PCR method for quantifying mixed cashmere and wool based on hair mitochondrial DNA. Text Res J2014; 84: 1612–1621.
6.
KirstenKGabrielCLotharKet al.Development of a DNA-analytical method for the identification of animal hair fibers in textiles. Text Res J2009; 79: 69–75.
7.
TonettiCVineisCAluigiAet al.Immunological method for the identification of animal hair fibres. Text Res J2012; 82: 766–772.
8.
SheFHKongLXNahavandiSet al.Intelligent animal fiber classification with artificial neural networks. Text Res J2002; 72: 594–600.
9.
MaCLiuXLiuF. A research on cashmere automatic identification method based on statistical analysis. Wool Text J2014; 42: 62–64.
10.
Shi X and Yu W. A new classification method for animal fibers. In: 2008 International Conference on Audio, Language and Image Processing, Shanghai, China, 7–9 July 2008, pp. 206–210. New York: IEEE.
11.
YuanYLiBMengMQH. Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Trans Autom Sci Eng2016; 13: 529–535.
12.
ZhuQZhongYZhaoBet al.Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery. IEEE Geosci Remote Sens Lett2016; 13: 747–751.
13.
Yang Y and Newsam S. Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th Sigspatial International Conference on Advances in Geographic Information Systems (ed AE Abbadi), San Jose, CA, USA, 2–5 November 2010, pp. 270–279. New York: ACM.
14.
Csurka G, Dance CR, Fan L, et al. Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision Eccv 2004 (ed D Comaniciu), Prague, Czech Republic, 11–14 May 2004, pp. 1–22. Berlin: Springer.
15.
Li FF and Perona P. A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 (ed C Schmid), San Diego, CA, USA, 20–25 June 2005, pp. 524–531. Los Alamitos: IEEE Computer Society.
16.
Caicedo JC, Cruz A, and Gonzalez FA. Histopathology image classification using bag of features and kernel functions. In: 12th Conference on Artificial Intelligence in Medicine (ed C Combic, Y Shahar, A Abu-Hanna), 18–22 July 2009, Verona, Italy, pp. 126–135. Berlin: Springer.
17.
Grauman K and Darrell T. The pyramid match kernel: discriminative classification with sets of image features. In: IEEE International Conference on Computer Vision 2005, Beijing, China, 17–20 October 2005, pp. 1458–1465. Los Alamitos: IEEE Computer Society.
18.
Lazebnik S, Schmid C, and Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006, New York, USA, 17–22 June 2006, pp. 2169–2178. Los Alamitos: IEEE Computer Society.
19.
Yang J, Yu K, Gong Y, et al. Linear spatial pyramid matching using sparse coding for image classification. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009, pp. 1794–1801. Los Alamitos: IEEE Computer Society.
20.
OtsuN. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern Soc1979; 9: 62–66.
21.
LoweDG. Distinctive image features from scale-invariant keypoints. Int J Comput Vision2004; 60: 91–110.
22.
MikolajczykKSchmidC. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell2005; 27: 1615–1630.
23.
Yang J, Jiang YG, Hauptmann AG, et al. Evaluating bag-of-visual-words representations in scene classification. In: 9th ACM Sigmm International Workshop on Multimedia Information Retrieval, Mir 2007, Augsburg, Bavaria, Germany, 28–29 September 2007, pp. 197–206. New York: ACM.
