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Studies from neuroscience show that part-mapping computations are employed byhuman visual system in the process of object recognition. In this work, wepresent an approach for analyzing semantic-part characteristics of objectcategory representations. For our experiments, we use category-epitome, arecently proposed sketch-based spatial representation for objects. To enablepart-importance analysis, we first obtain semantic-part annotations ofhand-drawn sketches originally used to construct the corresponding epitomes. Wethen examine the extent to which the semantic-parts are present in the epitomesof a category and visualize the relative importance of parts as a word cloud.Finally, we show how such word cloud visualizations provide an intuitiveunderstanding of category-level structural trends that exist in thecategory-epitome object representations.

Analyzing structural characteristics of object category representations from their semantic-part distributions