Training-free indexing refinement for visual media via multi-semantics

Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a focus on combining concepts by post-processing the outputs of individual concept detection. Due to the limitations and availability of training corpora which are usually sparsely and imprecisely labeled with concept groundtruth, training-based refinement methods for semantic indexing of visual media suffer in correctly capturing relationships between concepts, including co-occurrence and ontological relationships. In contrast to training-dependent methods which dominate this field, this paper presents a training-free refinement (TFR) algorithm for enhancing semantic indexing of visual media based purely on concept detection results, making the refinement of initial concept detections based on semantic enhancement, practical and flexible. This is achieved using what can be called multi-semantics, factoring in semantics from multiple sources. In the case of this paper, global and temporal neighbourhood information inferred from the original concept detections in terms of weighted non-negative matrix factorization and neighbourhood-based graph propagation are both used in the refinement of semantics. Furthermore, any available ontological concept relationships among concepts can also be integrated into this model as an additional source of external a priori knowledge. Extended experiments on two heterogeneous datasets, images from wearable cameras and videos from TRECVid, demonstrate the efficacy of the proposed TFR solution.