Density-based shape descriptors and similarity learning for 3D object retrieval (2007)
Abstract / truncated to 115 words
Next generation search engines will enable query formulations, other than text, relying on visual information encoded in terms of images and shapes. The 3D search technology, in particular, targets specialized application domains ranging from computer aided-design and manufacturing to cultural heritage archival and presentation. Content-based retrieval research aims at developing search engines that would allow users to perform a query by similarity of content. This thesis deals with two fundamentals problems in content-based 3D object retrieval: (1) How to describe a 3D shape to obtain a reliable representative for the subsequent task of similarity search? (2) How to supervise the search process to learn inter-shape similarities for more effective and semantic retrieval? Concerning the first ... toggle 5 keywords3d objet retrieval – 3d shape descriptors – kernel density estimation – statistical similarity learning – ranking risk minimization
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