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.
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition