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Reseach Article

Texture Features and KNN in Classification of Flower Images

Published on None 2010 by D S Guru, Y. H. Sharath, S. Manjunath
Recent Trends in Image Processing and Pattern Recognition
Foundation of Computer Science USA
RTIPPR - Number 1
None 2010
Authors: D S Guru, Y. H. Sharath, S. Manjunath
e3423fad-9e67-4362-ae71-30644db1a4d6

D S Guru, Y. H. Sharath, S. Manjunath . Texture Features and KNN in Classification of Flower Images. Recent Trends in Image Processing and Pattern Recognition. RTIPPR, 1 (None 2010), 21-29.

@article{
author = { D S Guru, Y. H. Sharath, S. Manjunath },
title = { Texture Features and KNN in Classification of Flower Images },
journal = { Recent Trends in Image Processing and Pattern Recognition },
issue_date = { None 2010 },
volume = { RTIPPR },
number = { 1 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 21-29 },
numpages = 9,
url = { /specialissues/rtippr/number1/972-95/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Recent Trends in Image Processing and Pattern Recognition
%A D S Guru
%A Y. H. Sharath
%A S. Manjunath
%T Texture Features and KNN in Classification of Flower Images
%J Recent Trends in Image Processing and Pattern Recognition
%@ 0975-8887
%V RTIPPR
%N 1
%P 21-29
%D 2010
%I International Journal of Computer Applications
Abstract

In this paper, we propose an algorithmic model for automatic classification of flowers using KNN classifier. The proposed algorithmic model is based on textural features such as Gray level co-occurrence matrix and Gabor responses. A flower image is segmented using a threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also, the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. Experimental Results are presented on a dataset of 1250 images consisting of 25 flower species. It is shown that relatively a good performance can be achieved, using KNN classifier algorithm. A qualitative comparative analysis of the proposed method with other well known existing flower classification methods is also presented.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Flower segmentation Gray Level Co-occurrence Matrix Gabor Responses Flower classification K Nearest neighbor classifier