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Data Analytics and Learning pp 221–233Cite as

Features Fusion for Retrieval of Flower Videos

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 43))

Abstract

This paper presents a Flower Video Retrieval System (FVRS). An algorithmic model is proposed for the retrieval of natural flower videos using Local Binary Pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM) as texture features and Scale-Invariant Feature Transform (SIFT) features. For a given query flower video, the system retrieves similar videos from the database using Multi-class Support Vector Machine (MSVM). Euclidean distance is used as a proximity measure. The proposed model has been verified on keyframes selected from cluster-based approaches from natural flower videos. Our own dataset is used for the experimentation, which consists of 1919 videos belonging to 20 classes of flowers. It has been observed that the proposed model generates good retrieval results from the fusion of the features SIFT, GLCM, and LBP.

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References

  1. Das, M., Manmatha, R., Riseman, E.M.: Indexing flower patent images using domain knowledge. IEEE Intell. Syst. 14(5), 24–33 (1999)

    Article  Google Scholar 

  2. Guru, D.S., Sharath Kumar, Y.H., Manjunath, S.: Texture Features and KNN in classification of flower images. Recent Trends Image Process. Pattern Recogn. RTIPPR IJCA Special Issue 21–29 (2010)

    Google Scholar 

  3. Guru, D.S., Sharath, Y.H., Manjunath, S.: Textural features in flower classification. Math. Comput. Model. 54(2011), 1030–1036 (2011)

    Article  Google Scholar 

  4. Shekar, B.H., Uma, K.P., Holla, Raghurama: Video clip retrieval based on LBP variance. Proc. Comput. Sci. 89(2016), 828–835 (2016)

    Article  Google Scholar 

  5. Weiming, H., Xie, N., Zeng, L.X., Stephen, M.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybernet. Part C Appl. Rev. 41(6), 797–819 (2011)

    Article  Google Scholar 

  6. Asha, S., Sreeraj, M.: Content based video retrieval using SURF descriptor. In: Third International Conference on Advances in Computing and Communications, pp. 212–215. IEEE (2013)

    Google Scholar 

  7. Nagaraja, G.S., Rajashekara Murthy, S., Deepak, T.S.: Content based video retrieval using support vector machine classification. In: 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 821–827. IEEE (2015)

    Google Scholar 

  8. Yingying, Z., Xiaoyan, H., Qiang, H., Tian, Qi: Large scale video copy retrieval with temporal concentration SIFT. Neurocomputing 187, 83–91 (2016)

    Article  Google Scholar 

  9. Li, D., Li-Zuo, J.: A video retrieval algorithm based on ensemble similarity. IEEE 638–642 ISBN 978-1-4244-6585 (2010)

    Google Scholar 

  10. Ionut, M., Bogdan, I., Jasper, U., Nicu, S.: Fisher kernel temporal variation-based relevance feedback for video retrieval. Comput. Vis. Image Understand. 143, 38–51 (2016)

    Article  Google Scholar 

  11. Bin, L., Wenbing, X., Xiang, L.: Design of video retrieval system using MPEG-7 descriptors. Sci. Verse ScienceDirect Proc. Eng. 29, 2578–2582 (2012)

    Google Scholar 

  12. Guru, D.S., Jyothi, V.K., Sharath Kumar, Y.H.: Cluster based approaches for keyframe selection in natural flower videos. In: 17th International Conference on Intelligent Systems Design and Applications, ISDA 2017, 14–16th, Dec. 2017, vol. 736, pp. 474–484. Springer, AISC (2017)

    Google Scholar 

  13. Gonzales, R.C, Woods, R.E, Eddins, S.L.: Digital Image Processing, 3rd ed. (2008)

    Google Scholar 

  14. Richard, N., Frank, N.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1–7 (2004)

    Article  Google Scholar 

  15. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybermat. SMC-3(6), 610–621 (1973)

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Loris, N., Alessandra, L., Sheyl, B.: Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39, 3634–3641 (2012)

    Article  Google Scholar 

  18. Arun Kumar, M., Gopal, M.: A hybrid SVM based decision tree. Pattern Recogn. 43, 3977–3987 (2010)

    Article  Google Scholar 

  19. Asnath, Y., Amutha, R.: Discrete cosine transform based fusion of multi-focus images for visual sensor networks. Signal Process. 95, 161–170 (2014)

    Article  Google Scholar 

  20. Bagher, B.A., Haghighat, A.A., Sevedarabi, H.: Multi-focus image fusion for visual sensor networks in DCT domain. Comput. Electr. Eng. 37(5), 789–797 (2011)

    Article  Google Scholar 

  21. Lowe, D.G.: Distinctive image features from scale-invariant keypoint. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Manasagangotri, Mysore, 570006, India

    D. S. Guru & V. K. Jyothi

  2. Department of Information Science, Maharaja Institute of Technology, Mysore (MITM), Mandya, 571438, India

    Y. H. Sharath Kumar

Authors
  1. D. S. Guru
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  2. V. K. Jyothi
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  3. Y. H. Sharath Kumar
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Corresponding author

Correspondence to V. K. Jyothi .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India

    P. Nagabhushan

  2. Department of Computer Science and Engineering, CBCS Education, University of Mysore, Mysuru, Karnataka, India

    D. S. Guru

  3. Department of Studies in Computer Science, Mangalore University, Mangalore, Karnataka, India

    B. H. Shekar

  4. Department of Information Science and Engineering, Maharaja Institute of Technology, Belawadi, Karnataka, India

    Y. H. Sharath Kumar

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Guru, D.S., Jyothi, V.K., Sharath Kumar, Y.H. (2019). Features Fusion for Retrieval of Flower Videos. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_19

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