International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
Thousands of images are generated every day, which implies the necessity to classify, organize and access them using an easy, faster and efficient way. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. Many different approaches concerning scene classification have been proposed in the last few years. This study presents a different approach using invariant moments and support vector machines to scene classification. Radial basis kernel function with p1 = 10 used for SVM. The results are proving the efficiency of this work with 83% classification rate. This complete study is carried out using real world data set.
How to cite this article:
V. Devendran , Amitabh Wahi and Hemalatha Thiagarajan . Invariant Moments to Scene Categorization Using Support Vector Machines.
DOI: https://doi.org/10.36478/ijscomp.2008.128.133
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2008.128.133