Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
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 image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Automatic Image Annotation and Retrieval Using Weighted Feature Selection
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Data Mining and Knowledge Discovery
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Given its importance, the problem of object discovery in High-Resolution Remote-Sensing (HRRS) imagery has been given a lot of attention by image retrieval researchers. Despite the vast amount of expert endeavor spent on this problem, more effort has been expected to discover and utilize hidden semantics of images for image retrieval. To this end, in this paper, we exploit a hyperclique pattern discovery method to find complex objects that consist of several co-existing individual objects that usually form a unique semantic concept. We consider the identified groups of co-existing objects as new feature sets and feed them into the learning model for better performance of image retrieval. Experiments with real-world datasets show that, with new semantic features as starting points, we can improve the performance of object discovery in terms of various external criteria.