Classification

In natural environments, different object categories usually co-occur (like car and pedestrian). Hence, when recognizing a particular object, not only the features extracted from that object are useful, but also identities of other objects visible in the scene might be informative. For example, one might often find a keyboard and a mouse next to a computer monitor; thus, these objects are often co-occurring, thereby jointly constituting an object-to-object context. One way of incorporating information about possibly noisy identities of other objects in the scene is to jointly optimize the identities of all detected objects using object classifier’s ranked or probabilistic beliefs about them. We use Markov Random Field (MRF) model to take into consideration natural or expected cooccurrences of different objects to refine the results of object category classifier. Moreover, we investigate social media mining as a rich source for extracting natural co-occurrences of objects and analyze its transferability when used to aid scene labeling in artificially created benchmark datasets. The idea of using spatial context relies on the fact that certain objects typically occur in specific environments or are likely to be close to other specific objects. Therefore, it becomes a necessity to model these object–object relationships. Recent research efforts in computer vision focus on how contextual relationships may improve object detection and recognition—especially in indoor environments.