Appearance Based Generic Object Modeling and Recognition Using Probabilistic Principal Component Analysis
Classifying unknown objects in familiar, general categories rather than trying to classify them into a certain known, but only similar class, or rejecting them at all is an important aspect in object recognition. Especially in tasks, where it is impossible to model all possibly appearing objects in advance, generic object modeling and recognition is crucial.
We present a novel approach to generic object modeling and classification based on probabilistic principal component analysis (PPCA). A data set can be separated into classes during an unsupervised learning step using the expectation-maximization algorithm. In contrast to principal component analysis the feature space is modeled in a locally linear manner. Additionally, Bayesian classification is possible thanks to the underlying probabilistic model.
The approach is applied to the COIL-20/100 databases. It shows that PPCA is well suited for appearance based generic object modeling and recognition. The automatic, unsupervised generation of categories matches in most cases the categorization done by humans. Improvements are expected if the categorization is performed in a supervised fashion.
KeywordsNear Neighbor Hierarchy Level Factor Analysis Model Unknown Object Category Match
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