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Efficient and Robust Retrieval by Shape Content through Curvature Scale Space

1998; Linguagem: Inglês

10.1142/9789812797988_0005

1793-0995

Farzin Mokhtarian, Sadegh Abbasi, Josef Kittler,

Medical Image Segmentation Techniques

Series on Software Engineering and Knowledge EngineeringImage Databases and Multi-Media Search, pp. 51-58 (1998) No AccessEfficient and Robust Retrieval by Shape Content through Curvature Scale SpaceFarzin Mokhtarian, Sadegh Abbasi, and Josef KittlerFarzin MokhtarianVision Speech and Signal Processing Group, Department of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, EnglandTel +44-1483-300800 extension 2288; Fax +44-1483-34139., Sadegh AbbasiVision Speech and Signal Processing Group, Department of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, England, and Josef KittlerVision Speech and Signal Processing Group, Department of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, Englandhttps://doi.org/10.1142/9789812797988_0005Cited by:67 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: We introduce a very fast and reliable method for shape similarity retrieval in large image databases which is robust with respect to noise, scale and orientation changes of the objects. The maxima of curvature zero-crossing contours of Curvature Scale Space (CSS) image are used to represent the shapes of object boundary contours. While a complex boundary is represented by about five pairs of integer values, an effective indexing method based on the aspect ratio of the CSS image, eccentricity and circularity is used to narrow down the range of searching. Since the matching algorithm has been designed to use global information, it is sensitive to major occlusion, but some minor occlusion will not cause any problems. We have tested and evaluated our method on a prototype database of 450 images of marine animals with a vast variety of shapes with very good results. The method can either be used in real applications or produce a reliable shape description for more complicated images when other features such as color and texture should also be considered. Since shape similarity is a subjective issue, in order to evaluate the method, we asked a number of volunteers to perform similarity retrieval based on shape on a randomly selected small database. We then compared the results of this experiment to the outputs of our system to the same queries and on the same database. The comparison indicated a promising performance of the system. FiguresReferencesRelatedDetailsCited By 67l-shaped geometry-based pattern descriptor serving shape retrievalS. Priyanka, Diego Oliva, Kethepalli Mallikarjuna and M.S. 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