Full text
| |
| Author(s): |
Cristiano Dalmaschio Ferreira
Total Authors: 1
|
| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Computação |
| Defense date: | 2007-07-31 |
| Examining board members: |
Ricardo da Silva Torres;
Altigran Soares da Silva;
Marcos André Gonçalves;
Neucimar Jerônimo Leite
|
| Advisor: | Ricardo da Silva Torres |
| Abstract | |
Relevance Feedback has been used to incorporate the subjectivity of user visual perception in content-based image retrieval tasks. The relevance feedback process consists in the following steps: (i) showing a small set of images; (ii) indication of relevant or irrelevant images by the user; (iii) and finally, learning the user needs from her feedback, and selecting a new set of images to be showed. This procedure is repeated until the user is satisfied. This dissertation presents two content-based image retrieval frameworks with relevance feedback. These frameworks employ Genetic Programming to discover a combination of descriptors that characterize the user perception of image similarity. The use of genetic programming is motivated by its capability of exploring the search space, which deals with the major goal of the proposed frameworks: find, among all combination functions of descriptors, the one that best represents the user needs. Several experiments were conducted to validate the proposed frameworks. These experiments employed three different images databases and color, shape and texture descriptors to represent the content of database images. The proposed frameworks were compared with three other content-based image retrieval methods regarding their efficiency and effectiveness in the retrieval process. Experiment results demonstrate the superiority of the proposed methods. The contributions of this work are: (i) study of different relevance feedback techniques; (ii) proposal of two content-based image retrieval frameworks with relevance feedback, based on genetic programming; (ii) implementation of the proposed methods and their validation with several experiments, and comparison with other methods (AU) | |