Distinguishing amateur and professional photographs

Abstract

Photography is the art of capturing and handling images. There are many ways to define the aesthetics in photography. The act of quantifying these aesthetic properties directly to distinguish photographs taken by amateur and professional photographers is almost impossible. This is because there is no general consensus. As such, it is beneficial to develop an algorithm that can differentiate the photographs.

In today’s technological advanced society, there are several researches done by computer scientist and engineers specialised in the field of image processing to learn aesthetic properties of the photographs. The properties are changed into computable image features for classification of photographs.

The project requires the author to understand and implement one of the research papers. The author furthers his reach by deriving new features he discovered upon learning more about photography. This allowed him to improve on the classification accuracy.

In this report, the author explains the various aesthetic appeals of photographs that are used for photograph classifications. The concept of computer vision and image processing to use to extract these aesthetic properties in order to convert into computable data and the concept of machine learning to train a model which is used to differentiate photographs are studied in order to fulfil this project’s requirement.

To determine the feasibility of the improved design, an application is implemented on MATLAB platform. It automatically takes in thousands of already classified photographs taken by professional and amateur photographer as training datasets and another set of randomly chosen picture as testing datasets. The program, once executed, allows the author to differentiate the photographs. The main features, design methodology and test specification of the application are discussed in this report.

Performances analysis of the implemented application is noted. The author also identified further areas that can be enhanced.