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| Author(s): |
Jurandy Gomes de Almeida Junior
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Computação |
| Defense date: | 2007-08-08 |
| Examining board members: |
Siome Klein Goldenstein;
Agma Juci Machado Traina;
Jorge Stolfi
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| Advisor: | Siome Klein Goldenstein; Ricardo da Silva Torres |
| Abstract | |
Advances in data storage, data transmission, and image acquisition have enabled the creation of large images datasets. This has spurred great interest for systems that are ablc to efficicntly rctricve images from these collections. This task has been addressed by thc so-called Content-Based Image Retrieval (CBIR) systems. ln these systems, image content is represented by their low-level features, such as color, shape, and texture. An ideal CBIR system should be effective and efficient. Effectiveness is achieved from image's abstract representations. ln general, traditional approaches for this process often fail in presence of different illumination, occlusion, and viewpoint conditions. Efficiency, on the other hand, is achieved from the organization given for these representations. ln general, data clustering approaches are one of the most useful techniques to reduce search space and speed up query processing. To address effectiveness issues, this work presents 81FT-Texton, a new method to incorporate illumination, occlusion, and viewpoint conditions into low-level features. This approach is based on discrete distributions of local invariant features and low-level image properties. With regard to efficiency issues, this work presents DAH-Cluster, a new clustering paradigm applied to CBIR. This approach combines features from both divisive and agglomerative hierarchical clustering paradigms. ln addition, DAH-Cluster introduces a new concept, called factor of reclustering, that allows grouping similar elements that would be separated by traditional clustering paradigms. Experiments show that the combination of these techniques allows the creation of a robust CBIR mechanism, achieving more effective and efficient results than traditional approaches in literature. The main contributions of this work are: (1) a new method for image retrieval that incorporates illumination, occ1usion, and viewpoint conditions into low-level features; and (2) a new data clustering paradigm that can be applied to information retrieval tasks (AU) | |