Abstract
In this paper we present a model for classification of pollen grain images based on surface texture. The surface textures of pollens are extracted using different models like Wavelet, Gabor, Local Binary Pattern (LBP), Gray Level Difference Matrix (GLDM) and Gray Level Co-Occurrence Matrix (GLCM) and combination of these features. The Nearest Neighbor (NN) classifier is adapted for classification. Unlike other existing contemporary works which are designed for a specific family or for one or few different families, the proposed model is designed independent of families of pollen grains. Experimentations on a dataset containing pollen grain images of about 50 different families totally 419 images of 18 classes have been conducted to demonstrate the performance of the proposed model. A classification rate up to 91.66 % is achieved when Gabor wavelet features are used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brummitt N, Bachman S (2010) Plants under pressure: a global assessment: first report of the IUCN sampled red list index for plants, Royal Botanic Gardens Kew, UK
Travieso MC, Briceno JC, Ticay-Rivas JR, Alonso JB (2011) Pollen classification based on contour features. In: Proceedings of 15th international conference on intelligent engineering systems, IEEE, Poprad, Slovakia
Shivanna KR (2003) Pollen biology and biotechnology—special, Indian edn. Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi
Kashinath B, Majumdar MR, Bhattacharya SG (2006) A textbook of palynology—(Basic and Applied). New Central Book Agency (P) Ltd., Kolkata
Takhtajan AL (1980) Outline of the classification of flowering plants (Magnoiophyta). Bot Rev 46:225–359
Araujo A, Perroton L, Oliveira R, Claudino L, Guimaraes S, Bastos, E (2001) Non linear features extraction applied to pollen images. In: Proceedings of nonlinear image processing and pattern analysis, XII, SPIE vol 4303
Li P, Flenley JR (1999) Pollen texture identification using neural networks. Int J Grana 38:59–64, ISSN 0017-3134
Damian M, Cernadas E, Formilla A, Otero PM (2004) Pollen classification of three types of plants of the family Urticaceae. In: Proceedings of 12th Portuguese conference on pattern recognition, Aveiro
Zhang Y, Fountain DW, Hodgson RM, Flenly JR, Gunetileke S (2004) Towards automation of palynology 3: pollen pattern recognition using Gabor transforms and digital moments. J Quat Sci 19:763–768, ISSN 0627-8179
Fernandez-Delgado M, Carrion P, Cernadas E, Galvez JF, Otero PM (2003) Improved classification of pollen texture images using SVM and MLP. In: Proceedings of international conference on visualization, imaging, and image processing, vol 2, Benalmadena, ES
Gonzalez RC, Woods RE (2009) Digital image processing, 3rd edn. Pearson-Prentice Hall Indian edition. Dorling Kindersley India Pvt.Ltd, New Delhi
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842
Ojala T, Pietikainen M (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Haralick RM, Shanmugam K, Dinstein I (1973) Textural Features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621
Guru DS, Sharath YH, Manjunath S (2010) Texture features and KNN in classification of flower images, IJCA special issue on “recent trends in image processing and pattern recognition”, RTIPPR
Kim JK, Park HW (1999) Statistical textural features for detection of micro calcifications in digitized mammograms. IEEE Trans Med Imaging 18(3):231–238
Olvera HF, Soriano SF, Hernandez EM (2006) Pollen morphology and systematic of Atripliceae (Chenopodiaceae). Int J Grana 45(3):175–194
Harley MM, Paton A, Harley RM, Cade PG (1992) Pollen Morphological studies in tribe Ocimeae (Nepetoideae: Labiatae): I. Ocimum L. Int J Grana 31(3):161–176
Remizowa MV, Sokoloff DD, Macfarlane TD, Yadav SR, Prychid CJ, Rudall PJ (2008) Comparative pollen morphology in the early divergent angiosperm family Hydatellaceae reveals variation at the infraspecific level. Int J Grana 47(2):81–100
Erdtman G (1966) Pollen morphology and Plant taxonomy in Angiosperms. Hafner Publishing Company, New York and London
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Guru, D.S., Siddesha, S., Manjunath, S. (2013). Texture in Classification of Pollen Grain Images. In: Swamy, P., Guru, D. (eds) Multimedia Processing, Communication and Computing Applications. Lecture Notes in Electrical Engineering, vol 213. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1143-3_7
Download citation
DOI: https://doi.org/10.1007/978-81-322-1143-3_7
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1142-6
Online ISBN: 978-81-322-1143-3
eBook Packages: EngineeringEngineering (R0)