Mixing hierarchical contexts for object recognition

Billy Peralta, Alvaro Soto

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva


Robust category-level object recognition is currently a major goal for the Computer Vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual information in the form of object co-ocurrences and spatial contraints has been successfully applied to reduce the inherent uncertainty of the visual world. Recently, Choi et al. [5] propose the use of a tree-structured graphical model to capture contextual relations among objects. Under this model there is only one possible fixed contextual relation among subsets of objects. In this work we extent Choi et al. approach by using a mixture model to consider the case that contextual relations among objects depend on scene type. Our experiments highlight the advantages of our proposal, showing that the adaptive specialization of contextual relations improves object recognition and object detection performances.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings
Número de páginas8
EstadoPublicada - 28 nov. 2011
Evento16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile
Duración: 15 nov. 201118 nov. 2011

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7042 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia16th Iberoamerican Congress on Pattern Recognition, CIARP 2011

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)


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