Probabilistic Modeling for Structural Change Inference

We view the task of change detection as a problem of object recognition from learning. The object is defined in a 3D space where the time is the 3rd dimension. We propose two competitive probabilistic models. The first one has a traditional regard on change, characterized as a ’presenceabsence’ within two scenes. The model is based on a logistic function, embedded in a framework called ’cut-andmerge’. The second approach is inspired from the Discriminative Random Fields (DRF) approach proposed by Ma and Hebert [KUMA2003]. The energy function is defined as the sum of an association potential and an interaction potential. We formulate the latter as a 3D anisotropic term. A simplified implementation enables to achieve fast computation in the 2D image space. In conclusion, the main contributions of this paper rely on : 1) the extension of the DRF to a 3D manifold ; 2) the cut-and-merge algorithm. The application proposed in the paper is on remote sensing images, for building c...

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Wei Liu, Véronique Prinet
• Keywords: ACCV 2006, Change Detection, Competitive Probabilistic Models, Computer Vision, Discriminative Random Fields