Efficient and Flexible Cluster-and-Search for CBIR

Autor(es)

Rocha, Anderson ; Almeida, Jurandy ; Nascimento, Mario A. ; Torres, Ricardo ; Goldenstein, Siome ; BlancTalon, J ; Bourennane, S ; Philips, W ; Popescu, D ; Scheunders, P

• Tipo de documento: Artigo Científico
• Fonte: Lecture Notes in Computer Science; v. 5259, p. 2-pg., 2008-01-01.

Resumo

Content-Based Image Retrieval is a challenging problem both in terms of effectiveness and efficiency. In this paper, we present a flexible cluster-and-search approach that is able to reuse any previously proposed image descriptor as long as a suitable similarity function is provided. In the clustering step, the image data set is clustered using a hybrid divisive-agglomerative hierarchical clustering technique. The obtained clusters are organized in a tree that can be traversed efficiently using the similarity function associated with the chosen image descriptors. Our experiments have shown that we can improve search-time performance by a factor of 10 or more, at the cost of small loss in effectiveness (typically less than 15%) when compared to the state-of-the-art solutions. (AU)

• Processo FAPESP: 05/58103-3 - Classificadores e aprendizado em processamento de imagens e visão computacional
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 05/52959-3 - Estudo e implementacao de tecnicas de descricao de modelos tridimensionais.
• Beneficiário: Jurandy Gomes de Almeida Junior
• Modalidade de apoio: Bolsas no Brasil - Mestrado