Fusión temprana de descriptores extraídos de mapas de prominencia multi-nivel para clasificar imágenes

E. Fidalgo, E. Alegre, L. Fernández-Robles, V. González-Castro

Resumen

En este artículo proponemos un método que permite mejorar la clasificación de imágenes en conjuntos de datos en los que la imagen contiene un único objeto. Para ello, consideramos los mapas de prominencia como si se trataran de mapas topográficos y filtramos las características del fondo de la imagen mejorando de esta forma la codificación que realiza sobre la imagen completa un modelo clásico basado en Bag of Visual Words (BoVW). En primer lugar, evaluamos seis conocidos algoritmos para la generación de mapas de prominencia y seleccionamos los métodos de GBVS y SIM al determinar que son los que retienen la mayor parte de la información del objeto. Utilizando la información de dichos mapas de prominencia eliminamos los descriptores SIFT extraídos de forma densa pertenecientes al fondo mediante el filtrado de características en base a imágenes binarias obtenidas a diversos niveles del mapa de prominencia. Realizamos el filtrado de descriptores obteniendo capas a diversos niveles del mapa de prominencia, y evaluamos la fusión temprana de los descriptores SIFT contenidos en dichas capas en cinco conjuntos de datos diferentes. Los resultados obtenidos en nuestra experimentación indican que el método propuesto mejora siempre al método de referencia cuando se combinan las dos primeras capas de GBVS o de SIM y el dataset contiene imágenes con un único objeto.

Palabras clave

Visión por computador; Algoritmos de detección; Aprendizaje máquina; Procesamiento de imágenes; Codificación; Clasificadores

Clasificación por materias

Visión por computador; Aprendizaje automático;

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