Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Essence of Neural Networks
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Vidya: an experiential annotation system
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
3D model search and retrieval using the spherical trace transform
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Image Processing
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Most existing Content-based Information Retrieval (CBIR) systems using semantic annotation, either annotate all the objects in a database (full annotation) or a manually selected subset (partial annotation) in order to increase the system's performance. As databases become larger, the manual effort needed for full annotation becomes unaffordable. In this paper, a fully automatic framework for partial annotation and annotation propagation, applied to 3D content, is presented. A part of the available 3D objects is automatically selected for manually annotation, based on their 'information content'. For the non-annotated objects, the annotation is automatically propagated using a neurofuzzy model, which is trained during the manual annotation process and takes into account the information hidden into the already annotated objects. Experimental results show that the proposed method is effective, fast and robust to outliers. The framework can be seen as another step towards bridging the semantic gap between low-level geometric characteristics (content) and intuitive semantics (context).