Publication

Combining attributes and Fisher vectors for efficient image retrieval

Conference Article

Conference

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Edition

2011

Pages

745-752

Doc link

http://dx.doi.org/10.1109/CVPR.2011.5995595

File

Download the digital copy of the doc pdf document

Abstract

Attributes were recently shown to give excellent results for category recognition. In this paper, we demonstrate their performance in the context of image retrieval. First, we show that retrieving images of particular objects based on attribute vectors gives results comparable to the state of the art. Second, we demonstrate that combining attribute and Fisher vectors improves performance for retrieval of particular objects as well as categories. Third, we implement an efficient coding technique for compressing the combined descriptor to very small codes. Experimental results on the Holidays dataset show that our approach significantly outperforms the state of the art, even for a very compact representation of 16 bytes per image. Retrieving category images is evaluated on the ''web-queries'' dataset. We show that attribute features combined with Fisher vectors improve the performance and that combined image features can supplement text features.

Categories

computer vision, image recognition.

Author keywords

image retrieval, attributes

Scientific reference

M. Douze, A. Ramisa and C. Schmid. Combining attributes and Fisher vectors for efficient image retrieval, 2011 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011, Colorado Springs, pp. 745-752, IEEE Press.