TY  - JOUR
T1  - Invariant Moments to Scene Categorization Using Support Vector Machines
AU - , V. Devendran AU - , Amitabh Wahi AU - , Hemalatha Thiagarajan 
JO  - International Journal of Soft Computing
VL  - 3
IS  - 2
SP  - 128
EP  - 133
PY  - 2008
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2008.128.133
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2008.128.133
KW  - Invariant moments
KW  -scene categorization
KW  -support vector machine
AB  - 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 p<SUB>1</SUB> = 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.
ER  -