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| Author(s): |
Pedro Henrique Bugatti
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2012-10-29 |
| Examining board members: |
Agma Juci Machado Traina;
João do Espírito Santo Batista Neto;
João Eduardo Ferreira;
Aparecido Nilceu Marana;
Paulo Mazzoncini de Azevedo Marques
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| Advisor: | Agma Juci Machado Traina |
| Abstract | |
In the last decade techniques for content-based image retrieval (CBIR) have been intensively explored due to the increase in the amount of capttured images and the need of fast retrieval of them. The medical field is a specific example that generates a large flow of information, especially digital images employed for diagnosing. One issue that still remains unsolved deals with how to reach the perceptual similarity. That is, to achieve an effectivs retrieval, one must characterize and quantify the perceptual similarity regarding the specialist in the field. Therefore, the present thesis was conceived tofill in this gap creating a consistent support to perform similarity queries over images, maintaining the semantics of a given query desired by tyhe user, bringing new contribuitions to the content-based retrieval area. To do so, three main methods were developed. The first methods applies a novel retrieval approach that integrates techniques of feature selection and relevance feedback to preform demand-driven feature selection guided by perceptual similarity, tuning the mining process on the fly, according to the user´s intention. The second method culminated in the development of approaches for harvesting and surveillance of user profiles, as well as new formulations to quantify the perceptual similarity of users , allowing to dynamically set the distance function that best fits the perception of a given user. The third method introduces a novel approach to enhance the retrieval process through user feedback and profiling, modifying the distance function in each feedback cycle choosing the best one for each cycle according to the user expectation. The experiments showed that the proposed metods effectively contributed to capture the perceptual similarity, improving in a great extent the image retrieval according to users´expectations (AU) | |
| FAPESP's process: | 08/00485-6 - Developing a Framework for Content-based Image Retrieval Integrating New Distance Functions, Relevance Feedback e User Profile to Answer Perceptual Similarity Queries |
| Grantee: | Pedro Henrique Bugatti |
| Support Opportunities: | Scholarships in Brazil - Doctorate |