Abstract
In this paper, combined image descriptors that can improve the performance of similar crop disease image retrieval system are suggested. When combining descriptors, the similarity between images is calculated using a single descriptor first. And, new similarity which corresponds to the combined descriptors is created by calculating the sum of image similarity corresponding to descriptors to be combined. Lastly, the image retrieval is carried out based on the distance value corresponding to the combined descriptors. The experiment was carried out with a total of 742 images of 3 crops including pear, grape and strawberry using the combined descriptors. As the experimental result, we discovered that using combined descriptors improved the system performance generally. And, we proved that a proper combination of descriptors varied for each crop and we found such combination. We also discovered that a combination of descriptors producing a high F-measure value of the system was different from a combination of descriptors having a higher probability that more accurate retrieval results would be outputted in the beginning of the screen. Therefore, proper combined descriptors should be selected according to actual system requirements.
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Zagoris, K., Chatzichristofis, S.A., Papamarkos, N., Boutalis, Y.S.: Automatic image annotation and retrieval using the joint composite descriptor. Informatics (PCI), 2010 14th Panhellenic Conference on. IEEE (2010)
Chatzichristofis, S.A., Boutalis, Y.S.: FCTH: fuzzy color and texture histogram-a low level feature for accurate image retrieval. In: Image Analysis for Multimedia Interactive Services, IEEE, pp. 191–196 (2008)
Lou, J., Lang, B., Tian, C., Zhang, D.: Image retrieval in the unstructured data management system AUDR. In: E-Science, 8th International Conference on. IEEE, pp. 1–7 (2012)
Kang, J.H., Jung, S.H., Nor, S.S., So, W.H., Sim, C.B.: Design and implementation of produce farming field-oriented smart pest information retrieval system based on mobile for u-Farm. J. Korea Inst. Electron. Commun. sci. 10, 1145–1156 (2015)
Van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1582–1596 (2010)
Ohm, J.R., Cieplinski, L., Kim, H.J., Krishnamachari, S., Manjunath, B.S., Messing, D.S., Yamada, A.: The MPEG-7 color descriptors. In: IEEE Transactions on Circuits and Systems for Video Technology (2001)
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Vamada, A.: Color and texture descriptors. IEEE Trans. Circuits Syst. Video Technol. 11, 703–715 (2001)
Chaudhary, M.D., Parul, V.P.: Multi-feature histogram intersection for efficient content based image retrieval. In: Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference on. IEEE, pp. 1366–1371 (2014)
CBIR: Texture Features (2017). https://www.cs.auckland.ac.nz/courses/compsci708s1c/lectures/Glect-html/topic4c708FSC.htm#castelli. Accessed 10 April (2017)
Riaz, F., Silva, F.B., Ribeiro, M.D., Coimbra, M.T.: Invariant gabor texture descriptors for classification of gastroenterology images. IEEE Trans. Biomed. Eng. 59, 2893–2904 (2012)
Yamamoto, K., Yamacuchi, O., Aoki, H..: Fast face clustering based on shot similarity for browsing video. In: Proceedings of the Progress in Informatics, Special issue: 3D image and video technology 7, pp. 53–62 (2010)
Bai, Y., Guo, L., Jin L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: 16th IEEE International Conference Image Processing (ICIP), pp. 3305–3308 (2009)
Mohanan, A. Raju, S.: A Survey on Different Relevance Feedback Techniques in Content Based Image Retrieval. (2017)
Wikipedia (2017). https://en.wikipedia.org/wiki/Content-based_image_retrieval#Content_comparison_using_image_distance_measures. Accessed 2 (Apr 2017)
Wikipedia (2017). https://en.wikipedia.org/wiki/MPEG-7. Accessed 2 (Apr 2017)
Multimedia Authoring Tool-06- Lee Ho-keun -MPEG7 Standardization (2016) https://www.google.co.kr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj8rrfss43TAhUCwbwKHaiCCIEQFggYMAA&url=http%3A%2F%2Fwww.minsang.com%2F~crazylim%2Funi_class%2Fms_data%2F%25B8%25D6%25C6%25BC%25B9%25CC%25B5%25F0%25BE%25EE%25C0%25FA%25C0%25DB%25C5%25F8-06-%25C0%25CC%25C8%25A3%25B1%25D9-MPEG7%25C7%25A5%25C1%25D8%25C8%25AD.hwp&usg=AFQjCNG3gQPjA8DUXQNiLodQ8mGOUKfWPQ&sig2=m_nL_HoYks8G9bV_d8_WQw&bvm=bv.151426398,d.dGc. Accessed 5 (Apr 2017)
Yun, S., Xiangfeng, W., Shanwen, Z., Chuanlei, Z.: PNN based crop disease recognition with leaf image features and meteorological data. Int. J. Agric. Biol. Eng 8, 60–68 (2015)
Prasad, S., Sateesh, S.K., Ghosh. D.: Energy efficient mobile vision system for plant leaf disease identification. In: Wireless Communications and Networking Conference (WCNC), IEEE, pp. 3314–3319 (2014)
Kim, J.: Optimized combinatorial clustering for stochastic processes. pp. 1–14. Cluster Computing (2017)
Kailey, K.S., Sahdra, G.S.: Content-Based Image Retrieval (CBIR) For Identifying Image Based Plant Disease (2012)
Karpe, T., Gore, D.: Discovery of plant disease based on content recovery from images. Int. J. Emerg. Trends Technol. Comput. Sci. 4, 65–69 (2015)
Baquero, D., Molina, J., Gil, R., Bojaca, C., Franco, H., Gomez, F.: An image retrieval system for tomato disease assessment. In: Image, Signal Processing and Artificial Vision (STSIVA), IEEE, pp. 1–5 (September 2014)
LIRe project homepage (2016). http://www.lire-project.net/. Accessed 10 May 2016
Lucene (2016). https://lucene.apache.org/core/documentation.html. Accessed 10 May 2016
Lux, M., Chatzichristofis, S.A.: Lire: lucene image retrieval: an extensible java cbir library. In: Proceedings of the 16th ACM international conference on Multimedia, ACM, pp. 1085–1088 (October 2008)
Yin, H., Jeong, D.W., Gu, Y.H., Yoo, S.J., Jeon, S.B.: A Diagnosis and Prescription System to Automatically Diagnose Pests. The Society of Digital Information (2016)
Yin, H., Yoo, S.J., Chung, W.H., Piao, Z.G., Gu, Y.H.: Mobile-based system design for crop disease diagnosis and treatment, (2016)
National Institute of Horticultural Herbal Science Home Page (2017). http://www.nihhs.go.kr/. Accessed (20 Jun 2017)
Lee, W., Leung, C.K.S., Lee, J.J.H.: Mobile web navigation in digital ecosystems using rooted directed trees. IEEE Trans. Ind. Electron. 58(6), 2154–2162 (2011)
Manjunath, B.S., et al.: Color and texture descriptors. IEEE Trans. Circuits Syst. Video Technol. 11, 703–715 (2001)
Acknowledgements
This work was partly supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through (Advanced Production Technology Development Program), funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA) (No 315091-3) and ICT R&D Program of MSIP/IITP [2014-0-00616, Building an Infrastructure of a Large Size Data Center].
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Piao, Z., Ahn, HG., Yoo, S.J. et al. Performance analysis of combined descriptors for similar crop disease image retrieval. Cluster Comput 20, 3565–3577 (2017). https://doi.org/10.1007/s10586-017-1145-4
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DOI: https://doi.org/10.1007/s10586-017-1145-4