A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Efficient top-k hyperplane query processing for multimedia information retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Speeding up active relevance feedback with approximate kNN retrieval for hyperplane queries
International Journal of Imaging Systems and Technology - Multimedia Information Retrieval
A posteriori multi-probe locality sensitive hashing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Relevance feedback in region-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Interactive learning of heterogeneous visual concepts with local features
Proceedings of the international conference on Multimedia
Interactive visual object search through mutual information maximization
Proceedings of the international conference on Multimedia
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This paper presents an efficient local features boosting strategy for interactive objects retrieval tasks such as on-line supervised learning or relevance feedback. The prediction time complexity of most existing methods is indeed usually linear in dataset size since the retrieval works by applying a trained classifier on the images of the dataset one by one. In our method, the trained classifier can be computed directly on the whole dataset in sublinear time thanks to distance-based weak classifiers. The idea is to speed-up drastically the prediction of each weak classifier on the whole dataset by performing approximate range queries with an efficient similarity search structure. Experiments on Caltech 256 dataset show that the technique is up to 250 times faster than the naive exhaustive method. Thanks to this efficiency improvement, we developed a relevance feedback mechanism on image regions freely selected by the user and we show how it improves the effectiveness of the retrieval.