Scene detection and recognition by analysing deep features using PlacesCNN

Scene recognition is employed for recognizing images along with some other visual features to collect information from it. As a field, it has turned out to be useful for digital marketers. Digital marketers can identify a consumer's favorite hangout spot like a cafe or bar based on his/her social media posts or uploads. Other applications include using the information from the pictures by the tour guide. CNNs help to identify whether the images belong to a specific class or not like a playground, classroom, dining room depending on the dataset. Different types of CNNs have been used to perform the classification task ranging from PlacesCNN, ImageNetCNN, HybridCNN and much more. PlacesCNN has been implemented using architectures namely AlexNet, GoogleNet and VGG. The objective of the paper is to study and analyze the performance for PlacesCNN based on VGG architecture to classify images into their correct classes along with determining the accuracy for the same. Using the pretrained model for PlacesCNN and the concept of transfer learning, we have been able to perform the task of scene recognition and achieve an accuracy of 98.25% for the same.