Large Field/Close-Up Image Classification: From Simple to Very Complex Features

Abstract

In this paper, the main contribution is to explore three different types of features including Exchangeable Image File (EXIF) features, handcrafted features and learned features in order to address the problem of large field/close up images classification with a Support Vector Machine (SVM) classifier. The impacts of every feature set on classification performances and computational complexities are investigated and compared to each other. Results prove that learned features are of course very efficient but with a computational cost that might be unreasonable. On the contrary, it appears that it is worthy to consider EXIF features when available because they represent a very good compromise between accuracy and computational cost.