Probabilistic framework of visual anomaly detection for unbalanced data.

This paper proposes a novel probabilistic detection framework of weighted combining semi-supervised k -means clustering and Posterior Probability SVM (PPSVM) for unbalanced data based on robot vision. Within the framework, an algorithm for learning synchronously the k in k -means and features is introduced based on hybrid wrapper and filter criterion. Then the optimal hierarchical probabilistic model by combining k -means and PPSVM is used to anomaly detection so as to alleviate the problems of imbalanced data with small samples, improve the detection accuracy, and deal with the difficult problem of defining the anomaly classes. The other contributions of our approach include the following three aspects: (1) it classifies anomaly candidates by using their class probability distributions rather than the direct extracted features; (2) the relevant classes are automatically built by learning the samples׳ multimodal Gaussian distribution; and (3) the cost-sensitive idea and filter criterion are integrated in learning k and features via cost function of Tabu search. Experimental results on real-world data sets show the proposed approach obtains a satisfactory detection performance within limited time in inspecting the condition of Heating, and Ventilation and Air-Conditioning (HVAC) ductwork. [ABSTRACT FROM AUTHOR]