Unsupervised learning of hierarchical semantics of objects (hSOs)

Abstract

A successful representation of objects in the literature is as a collection of patches, or parts, with a certain appearance and position. The relative locations of the different parts of an object are constrained by the geometry of the object. Going beyond the patches on a single object, consider a collection of images of a particular class of scenes containing multiple (recurring) objects. The parts belonging to different objects are not constrained by such a geometry. However the objects, arguably due to their semantic relationships, themselves demonstrate a pattern in their relative locations, which also propagates to their parts. Analyzing the interactions between the parts across the collection of images would reflect these patterns, and the parts can be grouped accordingly. These groupings are typically hierarchical. We introduce hSO: Hierarchical Semantics of Objects, which is learnt from a collection of images of a particular scene and captures this hierarchical grouping. We propose an approach for the unsupervised, learning of the hSO. The hSO simply holds objects, as clusters of patches, at its nodes, but it goes much beyond, that and also captures interactions between the objects through its structure. In addition to providing the semantic layout of the scene, learnt hSOs can have several useful applications such as providing context for enhanced object detection and compact scene representation for scene category classification.