Semi-Qualitative Probabilistic Networks in Computer Vision Problems

C. P. de Campos, L. Zhang, Y. Tong, Qiang Ji

Research output: Contribution to journalArticleAcademicpeer-review


This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision problems. Our version of SQPN allows qualitative influences and imprecise probability measures using intervals. We describe an Imprecise Dirichlet model for parameter learning and an iterative algorithm for evaluating posterior probabilities, maximum a posteriori and most probable explanations. Experiments on facial expression recognition and image segmentation problems are performed using real data.
Original languageEnglish
Pages (from-to)197-210
Number of pages14
JournalJournal of Statistical Theory and Practice
Publication statusPublished - 2009


Dive into the research topics of 'Semi-Qualitative Probabilistic Networks in Computer Vision Problems'. Together they form a unique fingerprint.

Cite this