A Comparative study for Feature Extraction and Classification of Images

Digital images increased rapidly due to the usage of users to the Internet, digital camera and their cell phones. The huge amounts of images are used for different purposes: business, education, recording/sharing events, remembering, etc. Most commercial and online shopping companies rely on images to attract consumers to visit and buy their products. Image classification is a core task for most of such applications. The performance of a classifier depends on two main factors: the features extracted from the images and the classification techniques. Many feature extraction algorithms are proposed by researchers such as Local Binary Pattern, Maximally Stable Extermal Regions, Speed Up Robust Features and Deep Convolutional Neural Networks. This paper tries to experimentally compare among these different feature extraction algorithms by applying different classifiers with each one. These classifiers are Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbors and Naive Bayes. The classifiers were learned from a dataset of eight categories: animals, food, nature, building, tourism, people, sport and apparatus. Our experiments show that SVM achieves 93.89% accuracy when Residual Neural Network is used as a feature detector and SVM as a classifier.