Abstract
A novel approach to model the semantic knowledge associated to objects detected in images is presented. The model is aimed at the classification of such objects according to contextual information combined to the extracted features. The system is based on Conditional Random Fields, a probabilistic graphical model used to model the conditional a-posteriori probability of the object classes, thus avoiding problems related to source modelling and features independence constraints. The novelty of the approach is in the addressing of the high-level, semantically rich objects interrelationships among image parts. This paper presents the application of the model to this new problem class and a first implementation of the system.
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Passino, G., Izquierdo, E. (2007). Conditional Random Fields for High-Level Part Correlation Analysis in Images. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds) Semantic Multimedia. SAMT 2007. Lecture Notes in Computer Science, vol 4816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77051-0_30
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DOI: https://doi.org/10.1007/978-3-540-77051-0_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77033-6
Online ISBN: 978-3-540-77051-0
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