Feature Extraction, Correspondence Regions and Image Retrieval using Structured Images
This thesis is about image descriptors, image retrieval and correspondence regions. The advantages of using scale-space on image descriptors are first discussed and a novel implementation of the sieve algorithm is introduced. We call this implementation 'The Structured Image'. It is shown...
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University of East Anglia
2008
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| Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502556 |
| Summary: | This thesis is about image descriptors, image retrieval and correspondence regions. The advantages of using scale-space on image descriptors are first discussed and a novel implementation of the sieve algorithm is introduced. We call this implementation 'The Structured Image'. It is shown here how such implementation decomposes the image in to a tree hierarchy collecting colour and texture descriptors throughout scale-space whilst remaining on a nearly linear order complexity. The algorithm is evaluated for correspondence repeatability rates and content based image retrieval. Results confirm the effectiveness of the implementation for both applications. We have also developed a graphic user interface to enable relevance feedback in to our image retrieval model. Our model is prepared to deal with segmentations of images rather than global att~ibutes of the image and it has been tested using two types of segmentations. Results in terms of precision rates are presented here for different iterations of relevance feedback. |
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