Abstract:
Images have always been affecting viewers on an emotional level by portraying so
much in a single frame. These emotions have 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 many areas like E-Health and E-learning etc. In
this paper, the emotional aspect of machines have been used to detect Geo-location 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, the
generic features also known as Low-Level features or Element-of-Art features are
extracted. Then, the Principle-of-Art features or Mid-Level features are extracted.
These features are easily understandable by humans. Experiments are then performed
on these two sets of features individually. These two sets are then combined together
to obtain resultant Hybrid features and same experiments are performed on them. On
comparison of results, it is indicated that the hybrid approach gives better accuracy
then the 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.
