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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...
In this paper we describe a Fisher vector encoding of images over Random Density Forests. Random Density Forests (RDFs) are an unsupervised variation of Random Decision Forests for density estimation. In this work we train RDFs by splitting at each node in order to minimize the Gaussian differential entropy of each split. We use this as generative model of image patch features and derive the Fisher...
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