Orientation Invariant Object Recognitions Using Geometric Moments Invariants and Color Histograms

Abstract

Object recognition is a very challenging task in artificial intelligence and robotics. Many approaches have been implemented to achieve this task with greater precision and accuracy. In this paper we have implemented the approach of detecting objects in images undergo with the change in scale, rotation, and orientation. Extracting Geometric moments invariant which are extensively use to extort global features and using color histogram approach we have improved the previously recognition rate to a significant measure. The accuracy of classification is increased by adding the new feature of color Histogram which is also an invariant feature for change in scale rotation, translation, and orientation of objects and using support vector machine learning algorithm for classification.