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| Author(s): |
Pedro Henrique Bugatti
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2008-04-16 |
| Examining board members: |
Agma Juci Machado Traina;
Carlos Hitoshi Morimoto;
Ricardo da Silva Torres
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| Advisor: | Agma Juci Machado Traina |
| Abstract | |
The retrieval of images by visual content relies on a feature extractor to provide the most meaningful intrinsic characteristics (features) from the data, and a distance function to quantify the similarity between them. A challenge in this field supporting content-based image retrieval (CBIR) to answer similarity queries is how to best integrate these two key aspects. There are plenty of researching on algorithms for feature extraction of images. However, little attention have been paid to the importance of the use of a well-suited distance function associated to a feature extractor. This Master Dissertation was conceived to fill in this gap. Therefore, herein it was investigated the behavior of different distance functions regarding distinct feature vector types. The three main types of image features were evaluated, regarding color distribution, texture and shape. It was also proposed two new techniques to perform feature selection over the feature vectors, in order to improve the precision when answering similarity queries. The first technique employed statistical association rules and achieve up to 38% gain in precision, while the second one employing the Shannon entropy achieved 71%, while siginificantly reducing the size of the feature vector. This work also showed that the proper use of a distance function effectively improves the similarity query results. Therefore, it opens new ways to enhance the acceptance of CBIR systems (AU) | |