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An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant

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Abstract

The main objective of this paper is to investigate the use of Quality Threshold ARTMAP (QTAM) neural network in classifying the feature vectors generated by moment invariant for the insect recognition task. In this work, six different types of moment invariant technique are adopted to extract the shape features of the insect images. These moment techniques are Geometrical Moment Invariant (GMI), United Moment Invariant (UMI), Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI), Tchebichef Moment Invariant (TMI) and Krawtchouk Moment Invariant (KMI). All the moment techniques are analyzed using the concept of intraclass and interclass analysis. In intraclass analysis, several computation methods are introduced in order to examine the invariance properties of adopted moment techniques for the same insect object. Meanwhile, the classification accuracy of neural networks is adopted to measure the interclass characteristic and the effectiveness of moment technique in extracting the shape features of insect images. Other types of neural networks are also utilized in this research work. This includes novel enhancement technique based on the Gaussian and Mahalanobis function that design to increase its prediction accuracy. All the other networks used to classify the feature vectors are based on the Fuzzy ARTMAP (FAM) neural network. The experimental results indicated that the Krawtchouk Moment Invariant technique generated the highest classification accuracy for most of the networks used and generated the smallest error for the intraclass analysis. Using different normalization technique, the Quality Threshold ARTMAP and Mahalanobis distance function (QTAM-m) network gave the highest insect recognition results when compared to other networks.

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References

  1. Arbuckle T, Schröder S, Steinhage V, Wittmann D (2001) Biodiversity informatics in action identification and monitoring of bee species using ABIS. In: Proceedings of sustainability in the information society, conf on environmental informatics

    Google Scholar 

  2. Affar AE, Ferdous K, Fadili HE, Qjidaa H (2009) Krawtchouk moment feature extraction for neural Arabic handwritten words recognition. In: International conference on multimedia computing and system, pp 443–448

    Chapter  Google Scholar 

  3. Andonie R, Sasu L (2006) Fuzzy ARTMAP with input relevances. IEEE Trans Neural Netw 17(4):929–941

    Article  Google Scholar 

  4. Baraldi A, Alpaydin E (2002a) Constructive feedforward ART clustering network—Part I. IEEE Trans Neural Netw 13(3):645–661

    Article  Google Scholar 

  5. Baraldi A, Alpaydin E (2002b) Constructive feedforward ART clustering network—part II. IEEE Trans Neural Netw 13(3):662–677

    Article  Google Scholar 

  6. Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992a) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5):698–713

    Article  Google Scholar 

  7. Carpenter GA, Grossberg S (1992b) A self-organizing neural network for supervised learning, recognition and prediction. IEEE Commun Mag 30(9):38–49

    Article  Google Scholar 

  8. Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB, Fuzzy ARTMAP (1992c) An adaptive resonance architecture for incremental learning of analog maps. In: International joint conference on neural networks. IJCNN, vol. 3, pp 309–314

    Google Scholar 

  9. Dai S, Huang H, Gao Z, Li K, Xiao S (2009) Vehicle-logo recognition method based on Tchebichef moment invariants and SVM. In: WRI world congress on software engineering, vol 3, pp 18–21

    Chapter  Google Scholar 

  10. Dehghan M, Faez K (1997) Farsi handwritten character recognition with moment invariants. In: 13th international conference on digital signal processing proceedings, vol 2, pp 507–510

    Chapter  Google Scholar 

  11. Do MT, Harp JM, Norris KC (1999) A test of a pattern recognition system for identification of spiders. Bull Entomol Res 89:217–224

    Article  Google Scholar 

  12. Fleyeh H, Dougherty M, Aenugula D, Baddam S (2007) Invariant road sign recognition with fuzzy Artmap and Zernike moments. In: IEEE intelligent vehicles symposium, pp 31–36

    Chapter  Google Scholar 

  13. Foulonneau A, Charbonnier P, Heitz F (2006) Affine-invariant geometric shape priors for region-based active contours. IEEE Trans Pattern Anal Mach Intell 28(8):1352–1357

    Article  Google Scholar 

  14. Francoy TM, Wittmann D, Drauschke M, Müller S, Steinhage V, Marcela A, Laure FB, Jong DD, Gonçalves LS (2008) Identification of Africanized honey bees through wing morphometrics: two fast and efficient procedures. Apidologie 39(5):488–494

    Article  Google Scholar 

  15. Gao Y, Song H, Tian X, Chen Y (2007) Identification algorithm of winged insects based on hybrid moment invariants. In: The 1st international conference on bioinformatics and biomedical engineering, pp 531–534

    Chapter  Google Scholar 

  16. Gonzalez-Carrasco I, Gracia-Crespo A, Ruiz-Mezcua B, Lopez-Cuadrado JL (2009) Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches. Appl Intell

  17. Haddadnia J, Faez K, Moallem P (2001) Neural network based face recognition with moment invariants. In: International conference on image processing, vol 1, pp 1018–1021

    Google Scholar 

  18. Heyer LJ, Kruglyak S, Yooseph S (1999) Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9:1106–1115

    Article  Google Scholar 

  19. Hu MK (1962) Visual pattern recognition by Moment invariants. IEEE Trans Inf Theory 8(2):179–187

    Article  MATH  Google Scholar 

  20. Juan XG, Ding WR (2009) A video zero-watermark algorithm against RST attacks. In: Asia-pacific conference on information processing, vol 2, pp 15–18

    Google Scholar 

  21. Larios N, Deng H, Zhang W, Sarpola M, Yuen J, Paasch R, Moldenke A, Lytle DA, Correa SR, Mortensen E, Shapiro LG, Dietterich TG (2007) Automated insect identification through concatenated histograms of local appearance features. In: IEEE workshop on applications of computer vision, pp 26–29

    Google Scholar 

  22. Li B, Meng MQH (2008) Computer aided detection of bleeding in capsule endoscopy images. In: Canadian conference on electrical and computer engineering, pp 1963–1966

    Chapter  Google Scholar 

  23. Liao SX, Pawlak M (1996) On image analysis by moments. IEEE Trans Pattern Anal Mach Intell 18(3):254–266

    Article  Google Scholar 

  24. Mayo M, Watson AT (2007) Automatic species identification of live moths. Knowledge-Based Syst 20(2):195–202

    Article  Google Scholar 

  25. Mukundan R, Ramakrishnan KR (1998) Moment functions in image analysis theory and applications. World Scientific, Singapore

    Book  MATH  Google Scholar 

  26. Mukundan R, Ong SH, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Trans Image Process 10(9):1357–1364

    Article  MathSciNet  MATH  Google Scholar 

  27. Nils H, Reinhard K, Warren A (2004) Footprint recognition of rodents and insects. Communication and information technology research technical report 149

  28. O’Neill MA (2000) DAISY: a practical tool for semi-automated species identification. In: MacLeod N (ed) Automated taxon identification in systematics: theory, approaches, and applications. CRC Press/Taylor & Francis Group, Boca Raton/Florida, pp 101–114

    Google Scholar 

  29. Ong LY, Chong CW, Besar R (2007) An approach to 3-D object recognition using Legendre moment invariants. In: International conference on intelligent and advanced systems, pp 671–674

    Chapter  Google Scholar 

  30. Potocnik B (2006) Assessment of region-based moment invariants for object recognition. In: 48th international symposium ELMAR-multimedia signal processing and communications, pp 27–32

    Google Scholar 

  31. Qing C, Petriu EM (2003) Optical character recognition for model-based object recognition applications. In: Proceedings the 2nd IEEE international workshop on haptic, audio and visual environments and their applications, pp 77–82

    Google Scholar 

  32. Radoi E, Quinquis A, Totir F (2006) Supervised self-organizing classification of superresolution ISAR images: an anechoic chamber experiment. EURASIP J Appl Signal Process 2006:1–16

    Google Scholar 

  33. Rajasekaran S, Vijayalakshmipai GA (2000) Image recognition using simplified fuzzy Artmap augmented with a moment based feature extractor. Int J Pattern Recognit Artif Intell 14:1081–1095

    Google Scholar 

  34. Rajasekaran S, Raj RA (2004) Image recognition using analog-ART1 architecture augmented with moment-based feature extractor. Neurocomputing 56:61–77

    Article  Google Scholar 

  35. Raj PA, Venkataramana A (2007) Radial Krawtchouk moments for rotational invariant pattern recognition. In: 6th international conference on information, communications & signal processing, pp 1–5

    Google Scholar 

  36. Raveendran P, Palaniappan R, Omatu S (2000) Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network. Inf Sci 130(1–4):67–84

    Article  MATH  Google Scholar 

  37. Ravi NK, Janakiraman TN, Thiagarajan H, Subaharan K (2007) Automated motion tracking of insects using invariant moments in image sequence. In: International conference on conference on computational intelligence and multimedia applications, vol 3, pp 220–224

    Google Scholar 

  38. Rizon M, Yazid H, Saad P, Shakaff AYM, Saad ARS, Mamat MR, Yaacob S, Desa H, Karthigayan M (2006) Object detection using geometric invariant moment. Am J Appl Sci 2(6):1876–1878

    Google Scholar 

  39. Ruberto CD, Morgera A (2008) Moment-based techniques for image retrieval. In: 19th international conference on database and expert systems application, pp 155–159

    Google Scholar 

  40. Sabaei M, Faez K (1997) Unsupervised classification of handwritten Farsi numerals using evolution strategies. In: IEEE region 10 annual conference speech and image technologies for computing and telecommunications, vol 1, pp 403–406

    Google Scholar 

  41. Saradha A, Annadurai S (2005) A hybrid feature extraction approach for face recognition systems. In: The international congress for global science and technology, vol 5, pp 23–30

    Google Scholar 

  42. Sheeba RJ, Devaraj D, Sukanesh R (2007) A novel feature extraction technique for face recognition. In: International conference on conference on computational intelligence and multimedia applications, vol 2, pp 428–435

    Google Scholar 

  43. Shi M, Haifeng W, Fleyeh H (2008) Support vector machines for traffic signs recognition. In: IEEE international joint conference on neural networks. IEEE world congress on computational intelligence, pp 3820–3827

    Google Scholar 

  44. Song L, Lin Y (2007) Study on the automatic instrumental reading system based on image processing. In: International conference on mechatronics and automation, pp 3870–3875

    Chapter  Google Scholar 

  45. Steinhage V, Arbuckle T, Schröder S, Cremers AB, Wittmann D (2001) ABIS: Automated identification of Bee species, BIOLOG workshop. German Programme on Biodivserity and Global Change, Status Report, German Ministry of Education and Research (BMBF), Bonn, pp 194–195

  46. Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920–930

    Article  MathSciNet  Google Scholar 

  47. Umbaugh SE (1998) Computer vision and image analysis a practical approach using CVIPtools. Prentice Hall, New Jersey

    Google Scholar 

  48. Venkataramana A, Raj PA (2007) Image watermarking using Krawtchouk moments. In: International conference on computing: theory and applications, pp 676–680

    Google Scholar 

  49. Vigdor B, Lerner B (2007) The Bayesian ARTMAP. IEEE Trans Neural Netw 18(6):1628–1644

    Article  Google Scholar 

  50. Watson A, O’Neill M, Kitching I (2004) Automated identification of live moths (Macrolepidoptera) using digital automated identification system. Syst Biodivers 1(3):287–300

    Article  Google Scholar 

  51. Wee CY, Paramesran R, Takeda F, Tsuzuki T, Kadota H, Shimanouchi S (2002) Classification of rice grains using fuzzy Artmap neural network. In: Asia-pacific conference on circuits and systems, vol 2, pp 223–226

    Chapter  Google Scholar 

  52. Wee CY, Paramesran R (2007) Derivation of blur-invariant features using orthogonal Legendre moments. Comput Vis 1(2):66–77

    Article  MathSciNet  Google Scholar 

  53. Weeks PJD, O’Neill MA, Gaston KJ, Gauld ID (1999) Species identification of wasps using principal component associative memories. Image Vis Comput 17(12):861–866

    Article  Google Scholar 

  54. Wen C, Guyer DE, Li W (2009) Local feature-based identification and classification for orchard insects. Biosyst Eng 104(3):299–307

    Article  Google Scholar 

  55. Wiliamson JR (1996) Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Netw 9(5):881–897

    Article  Google Scholar 

  56. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, Los Altos

    MATH  Google Scholar 

  57. Yaakob SN, Saad P (2007) Generalization performance analysis between fuzzy Artmap and Gaussian Artmap neural network. Malays J Comput Sci 20(1):13–22

    Google Scholar 

  58. Yaakob SN, Lim CP, Jain L (2010) A novel Euclidean quality threshold ARTMAP network and its application to pattern classification. J Neural Comput Appl 19(2):227–236

    Article  Google Scholar 

  59. Yap PT, Paramesran R, Ong S-H (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377

    Article  MathSciNet  Google Scholar 

  60. Yinan S, Liu W, Yuechao W (2003) United moment invariants for shape discrimination. In: IEEE proceedings on international conference on robotics, intelligent systems and signal processing, vol 1, pp 88–93

    Chapter  Google Scholar 

  61. Yu X, Zuorui S, Seishi N (2002) Measuring geometrical features of insect specimen using image analysis. In: Conference on Asian federation information in agriculture (AFITA)

    Google Scholar 

  62. Zhang H, Shu H, Han GN, Coatrieux G, Luo L, Coatrieux JL (2010) Blurred image recognition by Legendre moment invariants. IEEE Trans Image Process 19(3):596–611

    Article  MathSciNet  Google Scholar 

  63. Zhang L, Xiao WW, Ji Z (2007) Local affine transform invariant image watermarking by Krawtchouk moment invariants. Inf Secur 1(3):97–105

    Article  Google Scholar 

  64. Zhao J, Liu M, Yao M (2009) Study on image recognition of insect pest of sugarcane cotton aphis based on rough set and fuzzy c-means clustering. In: Third international symposium on intelligent information technology application, vol 2, pp 553–555

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University Malaysia Perlis, Perlis, Malaysia

    Shahrul Nizam Yaakob

  2. School of Electrical and Information Engineering, University of South Australia, Mount Gambier, Australia

    Lakhmi Jain

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  1. Shahrul Nizam Yaakob
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  2. Lakhmi Jain
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Correspondence to Shahrul Nizam Yaakob.

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Yaakob, S.N., Jain, L. An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant. Appl Intell 37, 12–30 (2012). https://doi.org/10.1007/s10489-011-0310-3

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