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Within the Content Based Image Retrieval (CBIR) framework, one of the main challenges is to tackle the scalability issues. We propose a new compact signature for similarity search. We use an original method to perform a high compression of signatures while retraining their effectiveness. We propose an embedding method that maps large signatures into a(More)
—The main issues of web scale image retrieval are to achieve a good accuracy while retaining low computational time and memory footprint. In this paper, we propose a compact image signature by aggregating tensors of visual descriptors. Efficient aggregation is achieved by preprocessing the descriptors. Compactness is achieved by projection and quantization(More)
This paper investigates the use of recent visual features based on second-order statistics, as well as new processing techniques to improve the quality of features. More specifically , we present and evaluate Fisher Vectors (FV), Vectors of Locally Aggregated Descriptors (VLAD), and Vectors of Locally Aggregated Tensors (VLAT). These techniques are combined(More)
In web-scale image retrieval, the most effective strategy is to aggregate local descriptors into a high dimensionality signature and then reduce it to a small dimensionality. Thanks to this strategy, web-scale image databases can be represented with small index and explored using fast visual similarities. However, the computation of this index has a very(More)
In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communications. The proposed algorithm relies on the method of matrix exponential learning (MXL) and only requires locally computable gradient observations that are possibly imperfect. To(More)
This paper presents MLBoost, an efficient method for learning to compare face signatures, and shows its application to the hierarchical organization of large face databases. More precisely, the proposed metric learning (ML) algorithm is based on boosting so that the metric is learned iteratively by combining several weak metrics. Boosting allows our method(More)
—In this paper, we tackle the storage and computational cost of linear projections used in dimensionality reduction for near duplicate image retrieval. We propose a new method based on metric learning with a lower training cost than existing methods. Moreover, by adding a sparsity constraint, we obtain a projection matrix with a low storage and projection(More)
This paper focuses on the problem of identitybased face retrieval [2], a problem heavily depending on the quality of the similarity function used to compare the images. Instead of using standard or handcrafted similarity functions, one of the most popular ways to address this problem is to learn adapted metrics, from sets of similar and dissimilar example(More)
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