Large-Scale Image Geo- Tagging Using Affective Classification
Muhammad Bilal Khan, Anis Ur Rahman · 2018
Images have always had a significant effect on their viewers at an emotional level by portraying so much in a single frame. These emotions have also been involved in human decision making. Machines can also be made emotionally intelligent using ‘Affective Computing’, giving them the ability of decision making by involving emotions. Emotional aspect of machine learning has been used in areas like E-Health and E-learning etc. In this paper, the emotional aspect of machines has been used to perform Geo-tagging of an image. The proposed solution concentrates on a hybrid approach towards Affective Image Classification where the Elements-of-Art based emotional features (EAEF) and Principles-of-Art based emotional features (PAEF) are combined. Firstly, experiments are performed on these two sets of features individually. Then, these two sets are combined to obtain a Hybrid feature vector and same experiments are performed on this feature vector. On comparison of results, it is indicated that the hybrid approach gives better accuracy then either individual approach. Images in this research work are downloaded from Yahoo Flickr Creative Commons 100 Million (YFCC100M) dataset which contains the co-ordinates of millions of images and are free to use.