Combining Features to a Class-Specific Model in an Instance Detection Framework

Object detection is a Computer Vision task that determines if there is an object of some category (class) in an image or video sequence. When the classes are formed by only one specific object, person or place, the task is known as instance detection. Object recognition classifies an object as belonging to a class in a set of known classes. In this work we deal with an instance detection/recognition task. We collected pictures of famous landmarks from the Internet to build the instance classes and test our framework. Some examples of the classes are: monuments, churches, ancient constructions or modern buildings. We tested several approaches to the problem and a new global feature is proposed to be combined to some widely known features like PHOW. A combination of features and classifiers to model the given instances in the training phase was the most successful one.