We proposed a novel approach to online estimation of generative models, which is based on probability density estimation. As the theoretical framework we use kernel density estimation (KDE). The method maintains and updates a non-parametric model of observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme. The results show that the online KDE outperforms or achieves a comparable performance to the state-of-the-art and produces models with a significantly lower complexity while allowing online adaptation.
COBISS.SI-ID: 8289876
In order for recognition systems to scale to a larger number of object categories building visual class taxonomies is important to achieve running times logarithmic in the number of classes. In this paper we propose a novel approach for speeding up recognition times of multi-class part-based object representations. The main idea is to construct a taxonomy of constellation models cascaded from coarse-to-fine resolution and use it in recognition with an effcient search strategy. The approach is utilized on the hierarchy-of-parts model.
COBISS.SI-ID: 8255828