Shape transformation-based image retrieval using non-uniform rational B-spline
| dc.contributor.author | Liang, Kim Meng | |
| dc.date.accessioned | 2016-11-01T02:54:15Z | |
| dc.date.available | 2016-11-01T02:54:15Z | |
| dc.date.issued | 2003-06 | |
| dc.description.abstract | Large image databases are used in many multimedia applications such as entertainment, business, art, engineering and science. Searching information in these databases is a crucial problem to be solved for the development of visual information system. Therefore efficient retrieval methods are required for the purpose of finding a desired image from a collection of images. This research is focussed on developing a shape-based image retrieval system for use in thematic databases. In thematic databases, all the images are highly similar to each other. For this purpose, a novel shape similarity matching methodology is needed in order to detect a small similarity difference between two images. This similarity difference is indicated by the computed similarity measure that is used to display a set of relevant images from the databases. The similarity measure introduced here bears resemblance to the human notion of'similarity. In the real-time shape-based image retrieval systems, the accuracy of the retrieval results and the retrieval time are two aspects that need to be looked into before any application is possible. In the past decade, shape similarity matching methodology has evolved and is influenced by two general approaches: feature vector approach and shape transformation approach. Feature vector approach provides fast retrieval time but does not ensure high accuracy retrieval result, whereas shape transformation approach has the ability to ensure high retrieval accuracy but it involves high computation time. Thus a new methodology, referred to as NURBS-warping approach is implemented by integrating the advantages of the two approaches. In the proposed approach, NonUniform Rational B-Spline (NURBS) and Gradient Vector Flow (GVF) are incorporated fo ensure accurate and fast retrieval results. NURBS is a compact and accurate shape descriptor, whereas GVF ensures fast and accurate matching results. The effectiveness of the NURBS-warping approach is examined by carrying out experiments on a collection of one thousand and one hundred highly similar images from a thematic fish database. The overall retrieval results show that the proposed approach is able to derive an accurate similarity measure that resembles human similarity judgement. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2936 | |
| dc.subject | Efficient retrieval methods are required for the purpose of | en_US |
| dc.subject | finding a desired image from a collection of images | en_US |
| dc.title | Shape transformation-based image retrieval using non-uniform rational B-spline | en_US |
| dc.type | Thesis | en_US |
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