Redução de dimensionalidade usando agrupamento e discretização ponderada para a recuperação de imagens por conteúdo

• Ano de defesa: 2012
• Autor(a) principal: Pirolla, Francisco Rocha
• Orientador(a): Ribeiro, Marcela Xavier
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• País: BR
• Palavras-chave em Português: Processamento de imagens; Discretização; Préprocessamento; CBIR; Transformação de características; Vetor de características; Agrupamento; Classificação
• Palavras-chave em Inglês: Feature transformation; Discretization; Pre-processing; Feature vector; Clustering; CBIR; Classification
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/511

This work proposes two new techniques of feature vector pre-processing to improve CBIR and image classification systems: a method of feature transformation based on the k-means clustering approach (Feature Transformation based on K-means - FTK) and a method of Weighted Feature Discretization - WFD. The FTK method employs the clustering principle of k-means to compact the feature vector space. The WFD method performs a weighted feature discretization, privileging the most important feature ranges to distinguish images. The proposed methods were employed to pre-process the feature vector in CBIR and in classification approaches, comparing the results with the pre-processing performed by PCA (a well known feature transformation method) and the original feature vector: FTK produced a reduction in the feature vector size with an improving in the query precision and a improvement in the classification accuracy; WFD improved the query precision up to and a improvement in the classification accuracy; the combination of WFD and FTK improved also the query precision and a improvement in the classification accuracy. These are very important results, especially when compared with PCA results, which leads to a minor reduction in the feature vector size, a minor increase in the query precision and a minor increase in the classification accuracy. Also the proposed approaches have linear computational cost where PCA has a cubic computational cost. The results indicate that the proposed approaches are well-suited to perform image feature vector pre-processing improving the overall quality of CBIR and classification systems.