Multimodal semantic-associative collateral labelling and indexing of still images
Zhu, M. and Badii, A. (2007) Multimodal semantic-associative collateral labelling and indexing of still images. In: 2007 International Workshop on Content-Based Multimedia Indexing, Proceedings. IEEE, New York, pp. 173-180. ISBN 9781424410101
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A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.