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Colour texture descriptor for CBIR of diseased tomato leaf images using modified local zigzag pattern

  • 1227: Content-based Image Retrieval
  • Published:
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Abstract

A novel colour texture extraction method called modified local zigzag pattern (MLZP) is suggested to efficaciously retrieve the content from diseased tomato leaf images. The presented descriptor is simple, yet a vigorous rotational invariant method which uses zigzag sampling for texture classification. This research work implements a method to retrieve images of leaves using colour, shape and texture feature descriptors. Initially, the directional edge information of a texture image is calculated in eight different directions using the Kirsch compass mask. Local Zigzag Pattern (LZP) and MLZP are later calculated using information relevant to edges. In general, the spatial Zigzag structure can be calculated by this pattern, based on values obtained for center pixel and the ones adjoining to it. Also, the system is robust to the illumination variations. HSV Colour Histogram is applied for extracting colour feature, while Scale Invariant Feature Transform (SIFT) is utilised to extract shape feature by matching Key points. Finally, the distance measure such as Euclidean, Cosine and Chi-square are used to find the distance between the features of the query image and the feature vector of the different diseased leaf images. All the samples with less distance value are retrieved for the query image. The retrieval efficiency of proposed CBIR system combined with MLZP feature extraction and Chi-square distance measure are achieving as 90%, 92%, 94%, 91% and 88% for bacterial spot, late blight, mosaic virus, septoria leaf spot and yellow leaf curl virus respectively.

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Data availability

The datasets analysed during the current study are available in the [PlantVillage dataset] repository, [https://www.kaggle.com/datasets/emmarex/plantdisease].

References

  1. Afifi AJ, Ashour WM (2012) "Content-Based Image Retrieval Using Invariant Color and Texture Features," 2012 International conference on digital image computing techniques and applications (DICTA), Fremantle, WA, pp. 1–6, https://doi.org/10.1109/DICTA.2012.6411665.

  2. Belloulata K, Belallouche L, Belalia A, Kpalma K (2014) "Region based image retrieval using Shape-Adaptive DCT," 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi’an, pp. 470–474, https://doi.org/10.1109/ChinaSIP.2014.6889287.

  3. Bhagat AP, Atique M (2012) "Design and development of systems for image segmentation and content based image retrieval," 2012 2nd National Conference on computational intelligence and signal processing (CISP), Guwahati, Assam, pp. 109–113, https://doi.org/10.1109/NCCISP.2012.6189688.

  4. Chen J et al (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720. https://doi.org/10.1109/TPAMI.2009.155

    Article  Google Scholar 

  5. Choudhary R, Raina N, Chaudhary N, Chauhan R, Goudar RH (2014) "An integrated approach to content based image retrieval," 2014 international conference on advances in computing, Communications and Informatics (ICACCI), New Delhi, pp. 2404-2410, https://doi.org/10.1109/ICACCI.2014.6968394.

  6. Dubey SR (2019) Face retrieval using frequency decoded local descriptor. Multimed Tools Appl 78:16411–16431. https://doi.org/10.1007/s11042-018-7028-8

    Article  Google Scholar 

  7. Dubey SR, Jalal AS (2016) Apple disease classification using color, texture and shape features from images. SIViP 10:819–826. https://doi.org/10.1007/s11760-015-0821-1

    Article  Google Scholar 

  8. Dubey SR, Singh SK, Singh RK (2015) Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases. IEEE Trans Image Process 24(12):5892–5903. https://doi.org/10.1109/TIP.2015.2493446

    Article  MathSciNet  MATH  Google Scholar 

  9. Ganar AN, Gode CS, Jambhulkar SM (2014) "Enhancement of Image Retrieval by Using Colour, Texture and Shape Features," 2014 International conference on electronic systems, Signal Processing and Computing Technologies, Nagpur, pp. 251–255, https://doi.org/10.1109/ICESC.2014.48.

  10. Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43(3):706–719

    Article  MATH  Google Scholar 

  11. 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. https://doi.org/10.1109/TIP.2010.2044957

    Article  MathSciNet  MATH  Google Scholar 

  12. Guo Z, Li Q, You J, Zhang D, Liu W (2011) Local directional derivative pattern for rotation invariant texture classification. Neural Comput & Applic 21:1893–1904

    Article  Google Scholar 

  13. Hiremath PS, Pujari J (2008) Content based image retrieval based on color, texture and shape features using image and its complement. Int J Comput Sci Secur 1(4):25–35

  14. Huang D, Shan C, Ardabilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):765–781. https://doi.org/10.1109/TSMCC.2011.2118750

    Article  Google Scholar 

  15. Kang J, Zhang W (2012) "A framework for image retrieval with hybrid features," 2012, 24th Chinese Control and Decision Conference (CCDC), Taiyuan, pp. 1326–1330, https://doi.org/10.1109/CCDC.2012.6244213.

  16. Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118. https://doi.org/10.1109/TIP.2009.2015682

    Article  MathSciNet  MATH  Google Scholar 

  17. Liu G-H, Yang J-Y (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46(1):188–198

    Article  Google Scholar 

  18. Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietikäinen M (2016) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381. https://doi.org/10.1109/TIP.2016.2522378

    Article  MathSciNet  MATH  Google Scholar 

  19. Patil JK, Kumar R (2017) Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Eng Agriculture, Environ Food 10(2):69–78

    Article  Google Scholar 

  20. Rasli RM, Muda TZT, Yusof Y, Bakar JA (2012) "Comparative Analysis of Content Based Image Retrieval Techniques Using Color Histogram: A Case Study of GLCM and K-Means Clustering," 2012 Third international conference on intelligent systems modelling and simulation, Kota Kinabalu, pp. 283–286, https://doi.org/10.1109/ISMS.2012.111.

  21. Redi M, Merialdo B (2011) "Saliency-aware color moments features for image categorization and retrieval," 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI), Madrid, pp. 199–204, https://doi.org/10.1109/CBMI.2011.5972545.

  22. Selvarajah S, Kodithuwakku SR (2011) "Combined feature descriptor for Content based Image Retrieval," 2011, 6th International Conference on Industrial and Information Systems, Kandy, pp. 164–168, https://doi.org/10.1109/ICIINFS.2011.6038060.

  23. Singh N, Singh K, Sinha AK (2012) A novel approach for content based image retrieval. Procedia Technol 4:245–250

    Article  Google Scholar 

  24. Singh C, Walia E, Kaur KP (2018) Color texture description with novel local binary patterns for effective image retrieval. Pattern Recog 76:50–68

    Article  Google Scholar 

  25. Varish N, Pal AK (2015) "Content based image retrieval using statistical features of color histogram," 2015 3rd international conference on signal processing, Communication and Networking (ICSCN), Chennai, pp. 1-6, https://doi.org/10.1109/ICSCN.2015.7219922.

  26. Vatamanu OA, Frandeş M, Lungeanu D, Mihalaş GI (2015) Content based image retrieval using local binary pattern operator and data mining techniques. Stud Health Technol Inform 210:75–79

    Google Scholar 

  27. Wenfei D, Shuchun Y, Songyu L, Zhiqiang Z, Wenbo G (2014) “Image retrieval based on multi-feature fusion,” 2014 fourth international conference on instrumentation and measurement. Computer, Communication and Control, Harbin, pp 240–243. https://doi.org/10.1109/IMCCC.2014.57

    Book  Google Scholar 

  28. Yasmin M, Sharif M, Irum I, Mohsin S (2014) An efficient content based image retrieval using EI classification and color features. J Appl Res Technol 12(5):877–885

    Article  Google Scholar 

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Authors and Affiliations

  1. Aditya Institute of Technology and Management, Tekkali, India

    Yogeswararao Gurubelli

  2. National Institute of Technology Tiruchirappalli, Tiruchirappalli, India

    Malmathanraj Ramanathan & Palanisamy Ponnusamy

Authors
  1. Yogeswararao Gurubelli
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  2. Malmathanraj Ramanathan
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  3. Palanisamy Ponnusamy
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Correspondence to Yogeswararao Gurubelli.

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Gurubelli, Y., Ramanathan, M. & Ponnusamy, P. Colour texture descriptor for CBIR of diseased tomato leaf images using modified local zigzag pattern. Multimed Tools Appl 82, 38077–38095 (2023). https://doi.org/10.1007/s11042-022-14292-y

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