Summary: Similarity-based retrieval from databases of isolated visual shapes has become an important information retrieval problem. The goal of the current work is to achieve high retrieval speed with reasonable retrieval effectiveness, and support for partial and occluded shape queries. In the proposed method, histograms of local shape parts are coded as index vectors. To increase retrieval accuracy, a rich set of parts at all scales of the shape is used; specifically, the parts are defined as connected sequences of regions in curvature scale space. To increase efficiency, structural indexing is used to compare the index vectors of the query and database shapes. In experimental evaluations, the method retrieved at least one similar shape in the top three retrieved items 99-100\% of the time, depending on the database. Average retrieval times ranged from \(0.7\) ms on a \(131\)-shape database to \(7\) ms on a \(1310\)-shape database. The method is thus suitable for fast, approximate shape retrieval in comparison with more accurate but more costly structural matching.