title: Object recognition and segmentation by a fragment-based hierarchy.
abstract: How do we learn to recognize visual categories, such as dogs and cats? Somehow, the brain uses limited variable examples to extract the essential characteristics of new visual categories. Here, I describe an approach to category learning and recognition that is based on recent computational advances. In this approach, objects are represented by a hierarchy of fragments that are extracted during learning from observed examples. The fragments are class-specific features and are selected to deliver a high amount of information for categorization. The same fragments hierarchy is then used for general categorization, individual object recognition and object-parts identification. Recognition is also combined with object segmentation, using stored fragments, to provide a top-down process that delineates object boundaries in complex cluttered scenes. The approach is computationally effective and provides a possible framework for categorization, recognition and segmentation in human vision.
authors.affiliation: Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot 76100, Israel. shimon.ullman@weizmann.ac.il
authors.forename: Shimon
authors.initials: S
authors.lastname: Ullman
mesh_terms: D005556:Form Perception / Q000502:physiology*; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D021641:Recognition, Psychology / Q000502:physiology*