Volume : V, Issue : XI, November - 2016

Classification Framework for an flower Image Retrieval System

Ms. S. Krishnaveni, Dr. A. Pethalakshmi

Abstract :

 In this paper, to analyze a new pattern classification model for classifying the quality of jasmine flower images using KNearest Neighbor (KNN) classifier. A flower image is segmented using a threshold based method. ?e data set has different jasmine flower species with similar appearance across different classes and varying appearance within a class. Also, the images of flowers are of different posing with a cluttered background under varying lighting conditions and climatic conditions. However, even when an image is sufficient, classifying a flower may still need a guidebook because with advances in digital and mobile technology it is easy to capture pictures of flowers, but it is still difficult to find out what they are. In order to know the name of a flower find more information about a flower on the web, but the link between obtaining an image of a flower and acquiring its name is missing. To assume that a training set of jasmine flower images with known class labels is available. However use an easy–to–compute low–level feature, banded color correlograms, which has been shown to be effective and efficient for content–based image retrieval.

Keywords :

Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Ms.S.Krishnaveni, Dr.A.Pethalakshmi, Classification Framework for an flower Image Retrieval System, International Journal of Scientific Research, Volume : 5 | Issue : 11 | November 2016

Number of Downloads : 438

References :