Context-Based Concept Fusion with Boosted Conditional Random Fields
The contextual relationships among different semantic concepts provide important information for automatic concept detection in images/videos. We propose a new context-based concept fusion (CBCF) method for semantic concept detection. Our work includes two folds. (1) We model the inter-conceptual relationships by a conditional random field (CRF) that improves detection results from independent detectors by taking into account the inter-correlation among concepts. CRF directly models the posterior probability of concept labels and is more accurate for the discriminative concept detection than previous statistical inferencing techniques. The boosted CRF framework is incorporated to further enhance performance by combining the power of boosting with CRF. (2) We develop an effective criterion to predict which concepts may benefit from CBCF. As reported in previous works, CBCF has inconsistent performance gain on different concepts. With accurate prediction, computational and data resources can be allocated to enhance concepts that are promising to gain performance. Evaluation on TRECVID2005 development set demonstrates the effectiveness of our algorithm.