Automatic Fruit Image Recognition System Based on Shape and Color Features

  • Hossam M. Zawbaa
  • Mona Abbass
  • Maryam Hazman
  • Aboul Ella Hassenian
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 488)

Abstract

This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.

Keywords

Fruit classification Image classification Features extraction K-Nearest Neighborhood (K-NN) Support Vector Machine (SVM) 
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References

  1. 1.
    Rege, S., Memane, R., Phatak, M., Agarwal, P.: 2D Geometric shape and color recognition using digital image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2(6), 2479–2481 (2013)Google Scholar
  2. 2.
    Oikonomidis, I., Argyros, A.A.: Deformable 2D shape matching based on shape contexts and dynamic programming. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009, Part II. LNCS, vol. 5876, pp. 460–469. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Elhariri, E., El-Bendary, N., Fouad, M.M.M., Platos, J., Hassanien, A.E., Hussein, A.M.M.: Multi-class SVM based classification approach for tomato ripeness. In: Abraham, A., Krömer, P., Snášel, V. (eds.) Innovations in Bio-inspired Computing and Applications. AISC, vol. 237, pp. 175–186. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  4. 4.
    Camargo, A., Smith, S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering 102(1), 9–21 (2009)CrossRefGoogle Scholar
  5. 5.
    Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S.: Automatic fruit and vegetable classification from images. Computers and Electronics in Agriculture 70(1), 96–104 (2010)CrossRefGoogle Scholar
  6. 6.
    Seng, W.C., Mirisaee, S.H.: A new method for fruits recognition system. In: International Conference on Electrical Engineering and Informatics, ICEEI 2009, Selangor, Malasia, pp. 130–134 (2009)Google Scholar
  7. 7.
    Aibinu, A.M., Salami, M.J.E., Shafie, A.A., Hazali, N., Termidzi, N.: Automatic fruits identification system using hybrid technique. In: Sixth IEEE International Symposium on Electronic Design, Test and Application (DELTA), Queenstown, pp. 217–221 (2011)Google Scholar
  8. 8.
    Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S.: Automatic produce classification from images using color, texture and appearance cues. In: XXI Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 2008, Campo Grande, pp. 3–10 (2008)Google Scholar
  9. 9.
    Shahbahrami, A., Borodin, D., Juurlink, B.: Comparison between color and texture features for image retrieval. In: Proceedings 19th Annual Workshop on Circuits, Systems and Signal Processing (ProRisc), Veldhoven, The Netherlands (2008)Google Scholar
  10. 10.
    Soman, S., Ghorpade, M., Sonone, V., Chavan, S.: Content based image retrieval using advanced color and texture features. In: IJCA Proceedings on International Conference in Computational Intelligence (ICCIA), New York, USA, vol. 9, pp. 1–5 (2012)Google Scholar
  11. 11.
    Mingqiang, Y., Kpalma, K., Ronsin, J.: A Survey of shape feature extraction techniques. Pattern Recognition, 43–90 (2008)Google Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Lowe, D.G.: Local feature view clustering for 3D object recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, Kauai, Hawaii, pp. 682–688 (2001)Google Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant key-points. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Juan, L., Gwun, O.: A comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing (IJIP) 3(4), 143–152 (2009)Google Scholar
  16. 16.
    Hamid, N., Yahya, A., Ahmad, R.B., Al-Qershi, O.M.: A Comparison between using SIFT and SURF for characteristic region based image steganography. International Journal of Computer Science Issues 9(3), 111–112 (2012)Google Scholar
  17. 17.
    Teoh, A., Samad, S.A., Hussain, A.: Nearest Neighbourhood Classifiers in Biometric Fusion. International Journal of the Computer, the Internet and Management 12(1), 23–36 (2004)Google Scholar
  18. 18.
    Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)CrossRefGoogle Scholar
  19. 19.
    Tzotsos, A., Argialas, D.: Support vector machine approach for object-based image analysis. In: Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography, pp. 663–677 (2008)Google Scholar
  20. 20.
    Wu, Q., Zhou, D.X.: Analysis of support vector machine classification. Journal of Computational Analysis & Applications 8(2), 99–119 (2006)MATHMathSciNetGoogle Scholar
  21. 21.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Abraham, A.: SVM-based Soccer Video Summarization System. In: Third World Congress Nature and Biologically Inspired Computing, Salamanca, Spain, pp. 7–11 (2011)Google Scholar
  22. 22.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Kim, T.-h.: Machine Learning-Based Soccer Video Summarization System. In: Kim, T.-h., Gelogo, Y. (eds.) MulGraB 2011, Part II. CCIS, vol. 263, pp. 19–28. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Berns, R.S.: Principles of Color Technology, 3rd edn. Wiley, New York (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hossam M. Zawbaa
    • 1
    • 2
    • 5
  • Mona Abbass
    • 3
    • 5
  • Maryam Hazman
    • 3
    • 5
  • Aboul Ella Hassenian
    • 4
    • 5
  1. 1.Faculty of Mathematics and Computer ScienceBabes-Bolyai UniversityRomania
  2. 2.Faculty of Computers and InformationBeni-Suef UniversityEgypt
  3. 3.Central Lab. for Agricultural Expert SystemAgricultural Research CenterEgypt
  4. 4.Faculty of Computers and InformationCairo UniversityEgypt
  5. 5.Scientific Research Group in Egypt (SRGE)Egypt

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