Reducing the semantic gap content-based image retrieval with similarity queries / Adequando consultas por similaridade para reduzir a descontinuidade semântica na recuperação de imagens por conteúdo

AUTOR(ES)

Humberto Luiz Razente

DATA DE PUBLICAÇÃO

2009

RESUMO

The increasing number of images captured in digital media fostered the developmet of new methods for the recovery of these images. Dissimilarity is a criteria that can be used for image retrieval, where the results are images that are similar to a given reference. The queries are based on feature vectors automatically extracted from the images and on distance functions to measure the dissimilarity between pair of vectors. Unfortunately, the search for images in simple queries may result in images that do not fulfill the user interest together with meaningful images, due to the semantic gap between the image features and to the subjectivity of the human interpretation. This problem leaded to the development of many methods to deal with the semantic gap. The focus of this thesis is the development of scalable methods aiming the semantic gap reduction in real time for content-based image retrieval systems. For this purpose, we present the formal definition of similarity queries based on multiple query centers in metric spaces to be used in relevance feedback methods, an exact method to optimize these queries and a model to deal with diversity in nearest neighbor queries including heuristics for its optimization

ASSUNTO(S)

diversity in nearest neighbor queries recuperação de imagens por conteúdo semantic gap descontinuidade semântica aggregate similarity queries diversidade em consultas aos vizinhos mais próximos consultas por similaridade agregada content-based image retrieval

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