Rotation-invariant image description from independent component analysis for classification purposes

• Autor(a) principal: Silva, Rodrigo Dalvit Carvalho da
• Data de Publicação: 2015
• Outros Autores: Thé, George André Pereira, Medeiros, Fátima Nelsizeuma Sombra de
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/70632
• topic: Independent component analysis; Invariant rotation; Pattern recognition

Independent component analysis (ICA) is a recent technique used in signal processing for feature description in classification systems, as well as in signal separation, with applications ranging from computer vision to economics. In this paper we propose a preprocessing step in order to make ICA algorithm efficient for rotation invariant feature description of images. Tests were carried out on five datasets and the extracted descriptors were used as inputs to the k-nearest neighbor (k-NN) classifier. Results showed an increasing trend on the recognition rate, which approached 100%. Additionally, when low-resolution images acquired from an industrial time-of-flight sensor are used, the recognition rate increased up to 93.33%.