Towards Training-Free Refinement for Semantic Indexing of Visual Media
Refereed Conference Meeting Proceeding
Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a fo- cus on combining concepts or post-processing the outputs of individual concept detection. Due to the limitations and availability of training cor- pora which are usually sparsely and imprecisely labeled, training-based refinement methods for semantic indexing of visual media suffer in cor- rectly 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 con- cept detections based on semantic enhancement, practical and flexible. This is achieved using global and temporal neighbourhood information inferred from the original concept detections in terms of weighted non- negative matrix factorization and neighbourhood-based graph propaga- tion, respectively. Any available ontological concept relationships can also be integrated into this model as an additional source of external a priori knowledge. Experiments on two datasets demonstrate the efficacy of the proposed TFR solution.
Proceedings of Multimedia Modelling, Miami, Fl, USA
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United States of America
Dublin City University (DCU)
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