Large-scale image retrieval with compressed Fisher vectors

作者：Florent Perronnin, Yan Liu, Jorge Sánchez, Hervé Poirier · 年份：2010 · DOI：10.1109/cvpr.2010.5540009 · 被引用次数：771 · 研究领域：Advanced Image and Video Retrieval Techniques、Image Retrieval and Classification Techniques、Robotics and Sensor-Based Localization

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.