Semantic grouping of visual features

Many current object class models build on visual parts that constitute an object. However, visually different entities may actually refer to the same object part. This may be harmful for part based object class models. We present a method how visually distinct parts with the same semantic role can be associated by creating groupings based on the similarity of their occurrence distributions. Experimental results verify that more compact class representations can be built based on these groupings, which lead to improved classification performance and/or reduced classification time.