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 B...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Florent Perronnin, Yan Liu, Jorge Sanchez, Herve Poirier
• Keywords: Computer Vision, CVPR 2010, Fisher Kernel Framework, Fisher Representation, Fisher Vectors