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Towards Training-Free Refinement for Semantic Indexing of Visual Media


Peng Wang, Lifeng Sun, Shiqang Yang, Alan Smeaton

Publication Type: 
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.
Conference Name: 
Multimedia Modelling
Proceedings of Multimedia Modelling, Miami, Fl, USA
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
United States of America
Research Group: 
Dublin City University (DCU)
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