Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Robust Scene Categorization by Learning Image Statistics in Context
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Video diver: generic video indexing with diverse features
Proceedings of the international workshop on Workshop on multimedia information retrieval
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
Although various kinds of image features have been proposed, there exists no single optimal feature which can save the effort of all other features for multimedia analysis applications, e.g. image annotation. In this paper, we propose a novel image representation, Semantic Gist (SemanGist), to combine the merit of multiple features automatically. Given a local image patch, SemanGist converts multiple low-level features of the patch into compact prediction scores of a few predefined semantic categories. To this end, a discriminative multi-label boosting algorithm is adopted. This local SemanGist output allows for incorporating semantic spatial context among adjacent patches. For applications like image annotation, this may further reduce possible annotation errors by considering the label compatibility. The same boosting algorithm is applied to the SemanGist representation, together with low-level features, to ensure the label compatibility. Experiments on an image annotation task show that SemanGist not only achieves compact representation but also incorporates spatial context at low run-time computational cost.