Multi Object Detection and Classification using Superpixel based Saliency Detection and Multi Support Vector Machine for Visually Impaired Person

Abstract

In recent decades, the object detection systems has gained significant research interest due to the progression in computer vision technology. Hence, there are many object recognition systems are developed for assisting visually impaired person, still there is a constant demand for better object recognition systems. In this paper, a new system is proposed for better object recognition and classification. Initially, the input images were collected from Caltech database. At first, the superpixel based saliency object detection method was used for segmenting the objects, because it gives more saliency information of an image with the advantage of color contrast.Then, feature extraction was accomplished by utilizing Local Ternary Pattern (LTP), colour moments, and Histogram of Oriented Gradient (HOG). After feature extraction, reliefF algorithm was used for rejecting the irrelevant features or for choosing the optimal features. At last, the optimal feature values were given as the input for Multi-Support Vector Machine (MSVM) classifier for classifying the individual objects. The experimental outcome showed that the proposed work improved the classification accuracy up to 1.5-2% as related to the existing work by means of accuracy.