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In this paper, we present an efficient method for visual descriptors retrieval based on compact hash codes computed using a multiple k-means assignment. The method has been applied to the problem of approximate nearest neighbor (ANN) search of local and global visual content descriptors, and it has been tested on different datasets: three large scale standard datasets of engineered features of up...
In this paper we present an efficient and effective method for visual descriptors hashing based on hierarchical multiple assignment within a k-means framework. The method has been used to address the problem of approximate nearest neighbor (ANN) retrieval, and it has been tested on local and global visual content descriptors, either engineered or learned. The proposed method has been compared to state-of-the-art...
In this paper we present an efficient and accurate method to aggregate a set of Deep Convolutional Neural Network (CNN) responses, extracted from a set of image windows. CNN features are usually computed on the whole frame or with a dense multi scale approach. There is evidence that using multiple windows yields a better image representation nonetheless it is still not clear how windows should be...
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