Texture in classification of pollen grain images

Guru, D. S. and Siddesha, S. and Manjunath, S. (2013) Texture in classification of pollen grain images. In: Multimedia Processing, Communication and Computing Applications.

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Official URL: http://doi.org/10.1007/978-81-322-1143-3_7

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Feature extraction, Communication, Texture features, Textures, Classification rates, Gabor wavelet features, Gray level co occurrence matrix(GLCM), Gray level differences, Image texture, Local binary patterns, Nearest Neighbor classifier, Pollen grains
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Arshiya Kousar
Date Deposited: 20 Sep 2019 05:41
Last Modified: 20 Sep 2019 05:41
URI: http://eprints.uni-mysore.ac.in/id/eprint/7986

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