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Shape based local affine invariant texture characteristics for fabric image retrieval

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Abstract

The rapid growth of fabric images needs fast retrieval for related applications, such as fashion design. The goal of fabric image retrieval is to retrieve and rank relevant fabrics from a large scale fabric database to visually assist users’ online shopping process in e-commerce. Most of existing solutions to this issue are not invariant with respect to 2D similarity or affine transformations, much less to 3D transformations of textured surface. In this paper, we propose a new search method with a shape based local affine invariant texture characteristics. By employing topographic map to represent fabric images, which is a complete, multi-scale and contrast invariant representation, the proposed method first obtains a tree of shapes from the topographic map. Then, a group of statistics is applied on these shapes to acquire a set of features that are invariant to 3D transformations. We finally represent these features combing relations between shapes, and based on the representation the similarity of pairs of fabric images can be estimated. To evaluate the performance of our algorithm, we conducted a series of experiments on a real-world fabric image dataset, and compared the proposed method with other previous ones. Experimental results demonstrate that the time of the proposed method spending in searching is less than 1 second, and meanwhile a higher PR score than others is obtained.

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References

  1. Bhatti N, Hanbury A, Stottinger J (2017) Contextual local primitives for binary patent image retrieval. Multimedia Tools and Applications 77(7):9111–9151

    Article  Google Scholar 

  2. Caselles V, Coll B, Morel JM (1999) Topographic maps and local contrast changes in natural images. Int J Comput Vis 33(1):5–27

    Article  Google Scholar 

  3. Chandy DA, Johnson JS, Selvan SE (2014) Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval. Multimedia Tools Appl 72(2):2011–2024

    Article  Google Scholar 

  4. Chen C, Young GK (1982) A study of texture classification using spectral features. Tech. rep., DTIC Document

  5. Chen CH, Pau LF, Wang PSP (2010) Handbook of pattern recognition and computer vision, vol 27 World Scientific

  6. Cohen FS, Fan Z, Patel MA (1991) Classification of rotated and scaled textured images using gaussian markov random field models. IEEE Trans Pattern Anal Mach Intell 13(2):192–202

    Article  Google Scholar 

  7. Costa AF, Humpire-Mamani G, Traina AJM (2012) An efficient algorithm for fractal analysis of textures. In: 25Th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), pp 39–46. IEEE

  8. Davarzani R, Mozaffari S, Yaghmaie K (2015) Scale- and rotation-invariant texture description with improved local binary pattern features. Sig Process 111:274–293

    Article  Google Scholar 

  9. Davis LS (1981) Polarograms: a new tool for image texture analysis. Pattern Recogn 13(3):219–223

    Article  Google Scholar 

  10. Davis LS, Johns SA, Aggarwal J (1979) Texture analysis using generalized co-occurrence matrices. IEEE Trans Pattern Anal Mach Intell PAMI-1(3):251–259

    Article  Google Scholar 

  11. Dharmagunawardhana C, Mahmoodi S, Bennett M, Niranjan M (2014) Gaussian markov random field based improved texture descriptor for image segmentation. Image Vis Comput 32(11):884–895

    Article  Google Scholar 

  12. Du JX, Zhai CM, Wang QP (2013) Recognition of plant leaf image based on fractal dimension features. Neurocomputing 116:150–156

    Article  Google Scholar 

  13. Fletcher ND, Evans AN (2005) Texture segmentation using area morphology local granulometries. In: Mathematical morphology: 40 years on, pp 367–376. Springer

  14. Gidas B (1989) A renormalization group approach to image processing problems. IEEE Trans Pattern Anal Mach Intell 11(2):164–180

    Article  MATH  Google Scholar 

  15. Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  MATH  Google Scholar 

  16. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804

    Article  Google Scholar 

  17. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187

    Article  MATH  Google Scholar 

  18. Ji Z, Wang J, Su Y, Song Z, Xing S (2013) Balance between object and background: Object-enhanced features for scene image classification. Neurocomputing 120:15–23

    Article  Google Scholar 

  19. Julesz B (1962) Visual pattern discrimination. IRE Trans Inf Theory 8(2):84–92

    Article  Google Scholar 

  20. Julesz B (1981) Textons, the elements of texture perception, and their interactions. Nature 290(5802):91–97

    Article  Google Scholar 

  21. Lan R, Zhong S, Liu Z, Shi Z, Luo X (2017) A simple texture feature for retrieval of medical images. Multimedia Tools and Applications 77(9):10853–10866

    Article  Google Scholar 

  22. Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278

    Article  Google Scholar 

  23. Leung T, Malik J (2001) Representing and recognizing the visual appearance of materials using three-dimensional textons. Int J Comput Vis 43(1):29–44

    Article  MATH  Google Scholar 

  24. Li Y, Xu S, Luo X, Lin S (2014) A new algorithm for product image search based on salient edge characterization. J Assoc Inf Sci Technol 65(12):2534–2551

    Article  Google Scholar 

  25. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  26. Malathi T, Bhuyan MK (2017) Performance analysis of gabor wavelet for extracting most informative and efficient features. Multimedia Tools Appl 76(6):8449–8469

    Article  Google Scholar 

  27. Mellor M, Hong BW, Brady M (2008) Locally rotation, contrast, and scale invariant descriptors for texture analysis. IEEE Trans Pattern Anal Mach Intell 30 (1):52–61

    Article  Google Scholar 

  28. Monasse P, Guichard F (2000) Fast computation of a contrast-invariant image representation. IEEE Trans Image Process 9(5):860–872

    Article  Google Scholar 

  29. Nelson J (2013) Fused lasso and rotation invariant autoregressive models for texture classification. Pattern Recogn Lett 34(16):2166–2172

    Article  Google Scholar 

  30. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  31. Pan Z, Li Z, Wu X (2018) A new encoding scheme of lbp based on maximum run length of state “1” for texture classification. Multimedia Tools and Applications 77 (20):26469–26484

    Article  Google Scholar 

  32. Pun CM, Lee MC (2003) Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell 25 (5):590–603

    Article  Google Scholar 

  33. Romeny BMH (2003) Front-end vision and multi-scale image analysis: multi-scale computer vision theory and applications, written in mathematica, vol 27. Springer Publishing Company, Incorporated

  34. Sandler R, Lindenbaum M (2009) Optimizing gabor filter design for texture edge detection and classification. Int J Comput Vis 84(3):308–324

    Article  Google Scholar 

  35. Serra J (1983) Image analysis and mathematical morphology. Academic Press, Inc., Orlando

    Google Scholar 

  36. Shen J (1997) Orthogonal gaussian-hermite moments for image characterization. In: Intelligent systems & advanced manufacturing, pp 224–233. International society for optics and photonics

  37. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: Proceedings of Ninth IEEE international conference on computer vision, pp 1470–1477. IEEE

  38. Soille P (1999) Morphological image analysis: principles and applications. Sens Rev 28(5):800–801

    MathSciNet  MATH  Google Scholar 

  39. Song Y, Zhang S, He B, Sha Q, Shen Y, Yan T, Nian R, Lendasse A (2018) Gaussian derivative models and ensemble extreme learning machine for texture image classification. Neurocomputing 277:53–64

    Article  Google Scholar 

  40. Sun A, Bhowmick SS, Nguyen N, Tran K, Bai G (2011) Tag-based social image retrieval: an empirical evaluation. J Amer Soc Inf Sci Technol 62(12):2364–2381

    Article  Google Scholar 

  41. Varma M, Zisserman A (2002) Classifying images of materials: Achieving viewpoint and illumination independence. In: Computer vision-ECCV 2002, pp 255–271. Springer

  42. Wang XY, Zhang BB, Yang HY (2014) Content-based image retrieval by integrating color and texture features. Multimedia Tools Appl 68(3):545–569

    Article  Google Scholar 

  43. Xia GS, Delon J, Gousseau Y (2010) Shape-based invariant texture indexing. Int J Comput Vis 88(3):382–403

    Article  MathSciNet  Google Scholar 

  44. Xu S, Jiang H, Lau FCM (2011) Retrieving and ranking unannotated images through collaboratively mining online search results. In: Proceedings of the 20th ACM international conference on Information and knowledge management, pp 485–494. ACM

  45. Yang B, Dai M (2011) Image analysis by gaussian–hermite moments. Sig Process 91(10):2290–2303

    Article  MATH  Google Scholar 

  46. Zhang J, Marszałek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238

    Article  Google Scholar 

  47. Zhang J, Tan T (2002) Brief review of invariant texture analysis methods. Pattern Recog 35(3):735–747

    Article  MATH  Google Scholar 

  48. Zhang Z, Liu S, Mei X, Xiao B, Zheng L (2017) Learning completed discriminative local features for texture classification. Pattern Recog 67:263–275

    Article  Google Scholar 

Download references

Acknowledgements

This research is jointly supported by the National Natural Science Foundation of China (U1504608, 61672471, 61762050, 61602222), and the Jiangxi Natural Science Foundation (No.20161BAB212043).

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Authors and Affiliations

  1. Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, 450001, China

    Yuhua Li, Jianwei Zhang, Ming Chen & Yan Huang

  2. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China

    Haopeng Lei

  3. East China Jiaotong University, Nanchang, 330022, China

    Guoliang Luo

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  1. Yuhua Li
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  2. Jianwei Zhang
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Correspondence to Jianwei Zhang.

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Li, Y., Zhang, J., Chen, M. et al. Shape based local affine invariant texture characteristics for fabric image retrieval. Multimed Tools Appl 78, 15433–15453 (2019). https://doi.org/10.1007/s11042-018-6936-y

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