Herve Poirier

Large-scale image retrieval with compressed Fisher vectors

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 the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speed-up the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach.

Large Scale URL-based Classification Using Online Incremental Learning

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 URLs for training and very large test sets (e.g., in the case of large-scale focused crawling). We propose an online incremental learning algorithm which addresses these issues. Our experiments based on large publicly available datasets demonstrate an improvement of 0.11 -- 0.12 in terms of F-measure over the baseline algorithms, like Support Vector Machine, in difficult scenarios where the cardinality of train set is just a fraction of that of the test set.

Vehicle type classification from laser scanner profiles: A benchmark of feature descriptors

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, or have taken the raw profiles for further pattern analysis. In this article, we focus on feature descriptors for supervised classification of laser scanner profiles. We evaluate a number of feature descriptors, including high-level features and raw profiles, but also introduce new descriptors. A 3D profile when interpreted as a 2D image with depth values as pixel intensities can benefit from recent advances in computer vision. Experiments on a real-world vehicle classification task indicate that the image-based descriptors, especially the Fisher vector, obtain improved performances with respect to high-level features and raw profiles.

Local contrast enhancement

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 artifacts and ensure the superior image quality of our results, as extensively validated by user evaluations. Unlike previous local contrast methods, ours is fully automatic in the sense that it can be directly applied to any input image with no parameter adjustment. This is because we exploit a trainable decision mechanism which classifies images as benefiting from enhancement or otherwise. Finally, a novel windowed TRC mechanism based on monotonic regression ensures that the algorithm takes only 0.3 s to process a 10 MPix image on a 3 GHz Pentium.