Scalable mining of small visual objects
Proceedings of the 20th ACM international conference on Multimedia
Query expansion enhancement by fast binary matching
Proceedings of the 20th ACM international conference on Multimedia
Social issue gives you an opportunity: discovering the personalised relevance of social issues
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
MatchMiner: efficient spanning structure mining in large image collections
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Mobile product image search by automatic query object extraction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Robust place recognition by avoiding confusing features and fast geometric re-ranking
CVM'12 Proceedings of the First international conference on Computational Visual Media
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
Find where you are: a new try in place recognition
The Visual Computer: International Journal of Computer Graphics
Ranking consistency for image matching and object retrieval
Pattern Recognition
Spatially aware feature selection and weighting for object retrieval
Image and Vision Computing
Generative Methods for Long-Term Place Recognition in Dynamic Scenes
International Journal of Computer Vision
Automatic shape expansion with verification to improve 3D retrieval, classification and matching
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model. All state-of-the-art retrieval results have been achieved by methods that include a query expansion that brings a significant boost in performance. We introduce three extensions to automatic query expansion: (i) a method capable of preventing tf-idf failure caused by the presence of sets of correlated features (confusers), (ii) an improved spatial verification and re-ranking step that incrementally builds a statistical model of the query object and (iii) we learn relevant spatial context to boost retrieval performance. The three improvements of query expansion were evaluated on standard Paris and Oxford datasets according to a standard protocol, and state-of-the-art results were achieved.