| dc.description.abstract | The rapid change in computer applications helps improving the efficiency of image processing techniques such as object recognition from multimedia. During the last few decades, many techniques were introduced by involving the interdisciplinary fields of computer science as a classification tool. In this paper, we used three different image classifiers techniques K- Nearest Neighbors (KNN), Support Vector Machine (SVM), and Earth Mover's Distance (EMD). These techniques require feature extraction, such as the Histogram of Oriented Gradient (HOG) algorithm. Regarding the datasets, we used COIL-100 dataset as a well-known dataset for Object recognition experiments. We divided the dataset into seven subsets. Then, we tested and compared the three algorithms using these subsets individually. Finally, we compared the results and We found that SVM and EMD are more efficient even though we used a large subset while KNN is affected when the dataset gets larger | en_US |
