Towards training-free refinement for semantic indexing of visual media
Wang, Peng, Sun, Lifeng, Yang, Shiqiang and Smeaton, Alan F.ORCID: 0000-0003-1028-8389
(2016)
Towards training-free refinement for semantic indexing of visual media.
Proceedings of Multimedia Modelling, Miami, Florida, 4-6 January 2016, LNCS 9
.
pp. 251-263.
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.