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The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of(More)
We address the problem of large-scale topic classification of web pages based on the minimal text available in the URLs. This problem is challenging because of the sparsity of feature vectors that are derived from the URL text, and the typical asymmetry between the cardinality of train and test sets due to non-availability of sufficient sets of annotated(More)
This article targets the problem of vehicle classification using laser scanner profiles, which is usually found as a component of electronic tolling systems. Laser scanners obtain a 3D measurement of the vehicle surface. Previous approaches have extracted high-level features (such as width, height, length and other measurements) from the scanner profiles,(More)
We introduce a novel algorithm for local contrast enhancement. The algorithm exploits a background image which is estimated with an edge-preserving filter. The background image controls a gain which enhances important details hidden in underexposed regions of the input image. Our designs for the gain, edge-preserving filter and chrominance recovery avoid(More)
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