Algorithms for clustering data
Algorithms for clustering data
Ten lectures on wavelets
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The story picturing engine: finding elite images to illustrate a story using mutual reinforcement
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Information Theory
Image classification for content-based indexing
IEEE Transactions on Image Processing
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Near-duplicate keyframe retrieval with visual keywords and semantic context
Proceedings of the 6th ACM international conference on Image and video retrieval
| Hi-index | 0.00 |
Learning semantics from annotated images to enhance content-based retrieval is an important research direction. In this paper,annotation data are assumed available for only a subset of images inside the database. An on the fly learning method is developed to capture the semantics of query images. Specifically, the semantics of annotated images in a visual proximity of a query are compared with each other to determine the amount of mutual endorsement. An image is considered endorsed by another if they possess similar semantics. Annotations with high mutual endorsement are used to narrow down a candidate pool of images. The new retrieval method is inherently dynamic and treats seamlessly different forms of annotation data. Experiments show that semantic endorsement can increase precision by as much as 70%in average for a wide range of parameter settings. We also develop a context provision mechanism to reveal the relationship between a query and semantic clusters extracted from the database. Context helps users explore the content of a database and provides a platform for them to tailor searches by stressing different perspectives in the interpretation of a query.