HOG feature descriptor based PCA with SVM for efficient & accurate classification of objects in image

In Computer vision, object recognition is a very important component and also very challenging. Intention of This paper is to exploit a high confidence object detection framework that boosts up the classification performance with less computational burden and cost efficient. Features are extracted from images by using Histograms of Oriented Gradients (HOG) technique and then for generating principle components as well as reducing dimensions Principal Component Analysis (PCA) has been applied on the extracted features. For classification of objects Support Vector Machine (SVM), Random Forest, Input mapped classifier, M5P classifier and Gaussian process classifier have been employed. A comparative study on performance of those approaches have been conducted. Moreover, for better clarification of the dataset, statistical and automated analysis have been considered. Overall findings demonstrates that, Principle Component Analysis (PCA)based Support Vector Machine (PSO) outperforms other approaches by depicting accuracy of 94.02% and highest F-Score measurement.