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In this paper, we present a region-based pLSA model to accomplish the task oftotal scene annotation. To be more specific, we not only properly generate alist of tags for each image, but also localizing each region with itscorresponding tag. We integrate advantages of different existing region-basedworks: employ efficient and powerful JSEG algorithm for segmentation so thateach region can easily express meaningful object information; the introductionof pLSA model can help better capturing semantic information behind thelow-level features. Moreover, we also propose an adaptive padding mechanism toautomatically choose the optimal padding strategy for each region, whichdirectly increases the overall system performance. Finally we conduct 3experiments to verify our ideas on Corel database and demonstrate theeffectiveness and accuracy of our system.

Adaptive Learning of Region-based pLSA Model for Total Scene Annotation