Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Kernel VA-files for relevance feedback retrieva
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Kernel Vector Approximation Files for Relevance Feedback Retrieval in Large Image Databases
Multimedia Tools and Applications
A novel framework for SVM-based image retrieval on large databases
Proceedings of the 13th annual ACM international conference on Multimedia
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Efficient top-k hyperplane query processing for multimedia information retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Active learning in very large databases
Multimedia Tools and Applications
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Computer Vision and Image Understanding
Optimization on active learning strategy for object category retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Inconsistency-based active learning for support vector machines
Pattern Recognition
Active graph matching based on pairwise probabilities between nodes
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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With the democratization of digital imaging devices, image databases exponentially grow. Thus, providing the user with a system for searching into these databases is a critical issue. However, bridging the semantic gap between which (semantic) concept(s) the user is looking for and the (semantic) content is quite difficult. In content-based image retrieval (CBIR) systems, a classic scenario is to formulate the user query, at first, with only one example (i.e. one image). In order to address this problem, active learning is a powerful technique which involves the user in interactively refining the query concept, through relevance feedback loops, by asking the user whether some strategically selected images are relevant or not. However, the complexity of state-of-the-art active learning methods is linear in the size of the database and thus dramatically slows down retrieval systems, when dealing with very large databases, which is no longer acceptable for users. In this article, we propose a strategy to overcome scalability limitations of active learning strategies by exploiting ultra fast k-nearest-neighbor (k-NN) methods, as locality sensitive hashing (LSH), and combining them with an active learning strategy dedicated to very large databases. We define a new LSH scheme adapted to @g^2 distance which often leads to better results in image retrieval context. We perform evaluation on databases between 5K and 180K images. The results show that our interactive retrieval system has a complexity almost constant in the size of the database. For a database of 180K images, our system is 45 times faster than exhaustive search (linear scan) reaching similar accuracy.