Learning the Taxonomy and Models of Categories Present in Arbitrary Images
This paper proposes, and presents a solution to, the problem of simultaneous learning of multiple visual categories present in an arbitrary image set and their inter-category relationships. These relationships, also called their taxonomy, allow categories to be defined recursively, as spatial configurations of (simpler) subcategories each of which may be shared by many categories. Each image is represented by a segmentation tree, whose structure captures recursive embedding of image regions in a multiscale segmentation, and whose nodes contain the associated region properties. The presence of any occurring categories is reflected in the occurrence of associated, similar subtrees within the image trees. Similar subtrees across the entire image set are clustered. Each cluster corresponds to a discovered category, represented by the cluster properties. A (subcategory) cluster of small matching subtrees may occur within multiple clusters (categories) of larger matching subtrees, in different spatial relationships with subtrees from other small clusters. Such recursive embedding, grouping and intersection of clusters is captured in a directed acyclic graph (DAG) which represents the discovered taxonomy. Detection, recognition and segmentation of any of the learned categories present in a new image are simultaneously conducted by matching the segmentation tree of the new image with the learned DAG. This matching also yields a semantic explanation of the recognized category, in terms of the presence of its subcategories. Experiments with a newly compiled dataset of four-legged animals demonstrate good cross-category resolvability.