Adaptive Learning of Region-based pLSA Model for Total Scene Annotation
This addresses scene annotation for computer vision applications, but it appears incremental as it integrates existing methods with minor enhancements.
The authors tackled total scene annotation by developing a region-based pLSA model that generates tags for images and localizes them to regions, using JSEG segmentation and an adaptive padding mechanism. They demonstrated effectiveness on the Corel database with three experiments.
In this paper, we present a region-based pLSA model to accomplish the task of total scene annotation. To be more specific, we not only properly generate a list of tags for each image, but also localizing each region with its corresponding tag. We integrate advantages of different existing region-based works: employ efficient and powerful JSEG algorithm for segmentation so that each region can easily express meaningful object information; the introduction of pLSA model can help better capturing semantic information behind the low-level features. Moreover, we also propose an adaptive padding mechanism to automatically choose the optimal padding strategy for each region, which directly increases the overall system performance. Finally we conduct 3 experiments to verify our ideas on Corel database and demonstrate the effectiveness and accuracy of our system.