Plant image retrieval using color, shape and texture features

Computer Journal

Volumn 54, Issue 9, 2011, Pages 1475-1490

  Kebapci, Hanife ^a   Yanikoglu, Berrin ^a   Unal, Gozde ^a  

^a SABANCI UNIVERSITY   (Turkey)

Author keywords

Gabor wavelets; image retrieval; plants; SIFT

Indexed keywords

A PLANTS; CONTENT-BASED IMAGE RETRIEVAL SYSTEM; GABOR WAVELETS; MATCHING TECHNIQUES; MAX-FLOW MIN-CUT; PLANT IDENTIFICATION; PLANTS; SHAPE FEATURES; SIFT; TEXTURE FEATURES;

COLOR MATCHING; FEATURE EXTRACTION; IMAGE RETRIEVAL; QUERY LANGUAGES; TEXTURES;

SEARCH ENGINES;

EID: 80052575312     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxq037     Document Type: Article

References (47)

1. Lowe, D.G. (1999) Object Recognition from Local Scale- Invariant Features. ICCV, pp. 1150-1157.
2. Lowe, D.G. (2004) Distinctive image features from scaleinvariant keypoints. Int. J. Comp. Vis., 60, 91-110.
3. Eakins, J.P. (2002) Towards intelligent image retrieval. Pattern Recognit., 35, 3-14.
4. Venters, C.C. and Cooper, D.M. (2000) A Review of Content- Based Image Retrieval Systems. Technical Report, Manchester Visualization Centre, Manchester Computing, University of Manchester, Manchester, UK.
5. Smeulders,A.W.M.,Worring, M., Santini, S., Gupta, A. and Jain, R. (2000) Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22, 1349-1380.
6. Tico, M., Haverinen, T. and Kuosmanen, P. (2000) A Method of Color Histogram Creation for Image Retrieval. Proc. Nordic Signal Processing Symp. (NORSIG'2000), Kolmarden, Sweden, June, pp. 157-160.
7. Gonzalez, R.C. and Woods, R.E. (2001) Digital Image Processing. Addison-Wesley, Boston, MA, USA.
8. El-ghazal, A., Basir, O.A. and Belkasim, S. (2007) Shapebased Image Retrieval Using Pair-wise Candidate Co-ranking. In Kamel, M.S. and Campilho, A.C. (eds), ICIAR, pp. 650-661. Lecture Notes in Computer Science, 4633. Springer.
9. Park, J.S. and Kim, T.-Y. (2004) Shape-based Image Retrieval Using Invariant Features. In Aizawa, K., Nakamura,Y. and Satoh, S. (eds) Advances in Multimedia Information Processing-PCM 2004, pp. 146-153. Lecture Notes in Computer Science. Springer, Berlin.
10. Abbasi, S., Mokhtarian, F. and Kittler, J. (1999) Curvature scale space image in shape similarity retrieval. Multimedia Syst., 7, 467-476.
11. Ma,W. and Manjunath, B. (1996) Texture Features and Learning Similarity. Proc. Conf. Computer Vis. Pattern Recognition (CVPR), June, pp. 425-430.
12. Han, J. and Ma, K.-K. (2007) Rotation-invariant and scaleinvariant gabor features for texture image retrieval. Image Vis. Comput., 25, 1474-1481.
13. Dengsheng Zhang, M.I., Aylwin, W. and Lu, G. (2000) Content Based Image Retrieval Using Gabor Texture Features. Proc. 1st IEEE Pacific Rim Conf. Multimedia (PCM'00), December, pp. 392-395.
14. Manjunath, B. and Ma,W. (1996) Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell., 18, 837-842.
15. Liu, C. and Wechsler, H. (2001) A Gabor Feature Classifier for Face Recognition. ICCV, Vol. 2, pp. 270-275.
16. Haralick, R.M. and Shapiro, L.G. (1992) Computer and Robot Vision. Addison-Wesley, Boston, MA, USA.
17. Flickner, M. et al. (1995) Query by image and video content: The QBIC system. IEEE Comput., 28, 23-32.
18. Bach, J.R., Fuller, C., Gupta, A., Hampapur, A., Horowitz, B., Humphrey, R., Jain, R.C. and Shu, C.F. (1996) Virage Image Search Engine: An Open Framework for Image Management. In Sethi, I.K. and Jain, R.C. (eds), Storage and Retrieval for Still Image and Video Databases IV, pp. 76-87. SPIE.
19. Lew, M.S., Sebe, N., Djeraba, C. and Jain, R. (2006) Contentbased multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl., 2, 1-19.
20. Veltkamp, R.C. and Tanase, M. (2000). Content-based image retrieval systems: A survey.
21. Yahiaoui, I., Hervé, N. and Boujemaa, N. (2006) Shape-Based Image Retrieval in Botanical Collections. PCM, pp. 357-364.
22. Wang, Z., Chi, Z. and Feng, D. (2003) Shape based leaf image retrieval. IEE Proc.-Vis., Image and Signal Process., 150, 34-43.
23. Wang, Z., Chi, Z., Feng, D. and Wang, Q. (2000) Leaf Image Retrieval with Shape Features. VISUAL'00: Proc. 4th Int. Conf. Advances in Visual Information Systems, London, UK, pp. 477- 487. Springer.
24. Backes, A.R. and Bruno, O.M. (2010) Shape classification using complex network and multi-scale fractal dimension. Pattern Recognit. Lett., 31, 44-51.
25. Bruno, O.M., Plotze, R.D.O., Falvo, M. and de Castro, M. (2008) Fractal dimension applied to plant identification. Inf. Sci., 178, 2722-2733.
26. Lin, F.-Y., Zheng, C.-H., Wang, X.-F. and Man, Q.-K. (2008) Multiple Classification of Plant Leaves Based on Gabor Transform and LBP Operator. ICIC, Vol. 3, pp. 432-439.
27. Man, Q.-K., Zheng, C.-H., Wang, X.-F. and Lin, F.-Y. (2008) Recognition of Plant Leaves Using Support Vector Machine. ICIC, Vol. 3, pp. 192-199.
28. Woebbecke, D.M., Meyer, G.E., Bargen, K.V. and Mortensen, D.A. (1993) Plant Species Identification, Size, and Enumeration Using Machine Vision Techniques on Near-Binary Images. Optics in Agriculture and Forestry, pp. 208-219. SPIE.
29. Yonekawa, S., Sakai, N. and Kitani, O. (1996) Identification of idealized leaf types using simple dimensionless shape factors by image analysis. Trans. ASAE, 39, 1525-2533.
30. Abbasi, S., Mokhtarian, F. and Kittler, J. (1997) Reliable Classification of Chrysanthemum Leaves Through Curvature Scale Space. SCALE-SPACE'97: Proc. 1st Int. Conf. Scale-Space Theory in Computer Vision, London, UK, pp. 284-295. Springer.
31. Pérez, A.J., López, F., Benlloch, J.V. and Christensen, S. (2000) Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric., 25, 197-212.
32. Jia, J. and Krutz, G. (1992) Location of the maize plant with machine vision. J. Agric. Eng. Res., 52, 169-181.
33. D.G. Sena Jr, Pinto, F.A.C., Queiroz, D.M. andViana, P.A. (2003) Fall armyworm damaged maize plant identification using digital images. Biosyst. Eng., 85, 449-454.
34. Boykov, Y. and Kolmogorov, V. (2004) An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26, 1124-1137.
35. Ford, L.R.F. and Fulkerson, D.R. (1956) Maximal flow through a network. Canad. J. Math., 8, 399-404.
36. Rother, C., Kolmogorov, V. and Blake, A. (2004) "Grabcut": Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph., 23, 309-314.
37. Zarit, B., Super, B. and Quek, F. (1999) Comparison of Five Color Models in Skin Pixel Classification. Proc. Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58-63.
38. Sural, S., Qian, G. and Pramanik, S. (2002) Segmentation and Histogram Generation Using the HSV Color Space for Image Retrieval. ICIP, Vol. 2, pp. II-589-II-592.
39. Shim, S.-O. and Choi, T.-S. (2003) Image Indexing by Modified Color Co-occurrence Matrix. ICIP, September, Vol. 2, pp. III- 493-III-496.
40. Jain, A.K. andVailaya, A. (1996) Image retrieval using color and shape. Pattern Recognit., 29, 1233-1244.
41. Ledwich, L. and Williams, S. (2004) Reduced SIFT Features for Image Retrieval and Indoor Localisation. Australian Conf. Robotics and Automation.
42. Wang, J., Zha, H. and Cipolla, R. (2005) Combining Interest Points and Edges for Content-Based Image Retrieval. IEEE Int. Conf. Image Processing, 2005. ICIP 2005, September, pp. 1256- 1259.
43. Šonka, M., Hlaváč,V. and Boyle, R.D. (1993) Image Processing, Analysis and MachineVision (1st edn). Chapman&Hall, London, UK.
44. Iqbal, Q. and Aggarwal, J.K. (2002) Combining Structure, Color andTexture for Image Retrireval:APerformance Evaluation. 16th Int. Conf. Pattern Recognition (ICPR), August, pp. 438-443.
45. Arevalillo-Herráez, M., Domingo, J. and Ferri, F.J. (2008) Combining similarity measures in content-based image retrieval. Pattern Recognit. Lett., 29, 2174-2181.
46. Plataniotis, K.N. andVenetsanopoulos, A.N. (2000) Color Image Processing and Applications. Springer NewYork, USA.
47. Wong, Y.M., Hoi, S. and Lyu, M. (2007) An Empirical Study on Large-Scale Content-based Image Retrieval. IEEE Int. Conf. Multimedia and Expo, July, pp. 2206-2209.