Comparação de técnicas para a determinação de semelhança entre imagens digitais

• Ano de defesa: 2008
• Autor(a) principal: Tannús, Marco Túlio Faissol
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Semelhança; Histograma; MPEG-7; Matriz de confusão; Taxa média de retorno modificada e normalizada; Processamento de imagens - Técnicas digitais; Banco de dados; Similarity; Histogram; Confusion matrix; Average normalized modified retrieval rate; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14388

The retrieval of similar images in databases is a wide and complex research field that shows a great demand for good performance applications. The increasing volume of information available in the Internet and the success of textual search engines motivate the development of tools that make possible image searches by content similarity. Many features can be applied in determining the similarity between images, such as size, color, shape, color variation, texture, objects and their spatial distribution, among others. Texture and color are the most important features which allow a preliminary analysis of image similarity. This dissertation presents many techniques introduced in the literature, which analyze texture and color. Some of them were implemented, their performances were compared and the results were presented. This comparison allows the determination of the best techniques, making possible the analysis of their applicability and can be used as a reference in future works. The quantitative performance analyses were done using the ANMRR metric, defined in the MPEG-7 standard, and the confusion matrices were presented for each of the tested techniques. Two groups of quantitative tests were realized: the first one was applied upon a gray scale texture database and the second one, upon a color image database. For the experiment with the gray scale texture images, the techniques PBLIRU16, MCNC and their combination presented the best performances. For the experiment with the color images, SCD, HDCIG and CSD techniques performed best.