Abstract
This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This leads us to derive a more precise representation based on Hamming embedding (HE) and weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within an inverted file and are efficiently exploited for all images in the dataset. We then introduce a graph-structured quantizer which significantly speeds up the assignment of the descriptors to visual words. A comparison with the state of the art shows the interest of our approach when high accuracy is needed.
Experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short-list of images, is shown to be complementary to our weak geometric consistency constraints. Our approach is shown to outperform the state-of-the-art on the three datasets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andoni, A., Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. (2006). Nearest-neighbor methods in learning and vision: theory and practice. Cambridge: MIT Press.
Chum, O., Philbin, J., Sivic, J., Isard, M., & Zisserman, A. (2007). Total recall: Automatic query expansion with a generative feature model for object retrieval. In International conference on computer vision.
Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. (2004). Locality-sensitive hashing scheme based on p-stable distributions. In Symposium on computational geometry (pp. 253–262).
Douze, M., Jégou, H., Singh, H., Amsaleg, L., & Schmid, C. (2009). Evaluation of gist descriptors for web-scale image search. In Conference on image and video retrieval.
Fraundorfer, F., Stewénius, H., & Nistér, D. (2007). A binning scheme for fast hard drive based image search. In Conference on computer vision and pattern recognition.
Jégou, H., & Douze, M. (2008). INRIA Holidays dataset. http://lear.inrialpes.fr/people/jegou/data.php.
Jégou, H., Harzallah, H., & Schmid, C. (2007). A contextual dissimilarity measure for accurate and efficient image search. In Conference on computer vision and pattern recognition.
Jégou, H., Douze, M., & Schmid, C. (2008). Hamming embedding and weak geometric consistency for large scale image search. In European conference on computer vision.
Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 77–116.
Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Matas, J., Chum, O., Martin, U., & Pajdla, T. (2002). Robust wide baseline stereo from maximally stable extremal regions. In British machine vision conference (pp. 384–393).
Mikolajczyk, K. (2007). Binaries for affine covariant region descriptors. http://www.robots.ox.ac.uk/~vgg/research/affine/.
Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86.
Muja, M., & Lowe, D. G. (2009). Fast approximate nearest neighbors with automatic algorithm configuration. In International conference on computer vision and applications.
Nistér, D., & Stewénius, H. (2006). Scalable recognition with a vocabulary tree. In Conference on computer vision and pattern recognition (pp. 2161–2168).
Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3), 145–175.
Omercevic, D., Drbohlav, O., & Leonardis, A. (2007). High-dimensional feature matching: employing the concept of meaningful nearest neighbors. In International conference on computer vision.
Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In Conference on computer vision and pattern recognition.
Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2008). Lost in quantization: Improving particular object retrieval in large scale image databases. In Conference on computer vision and pattern recognition.
Schindler, G., Brown, M., & Szeliski, R. (2007). City-scale location recognition. In Conference on computer vision and pattern recognition.
Sivic, J., & Zisserman, A. (2003). Video Google: A text retrieval approach to object matching in videos. In International conference on computer vision (pp. 1470–1477).
Torralba, A., Fergus, R., & Weiss, Y. (2008). Small codes and large databases for recognition. In Conference on computer vision and pattern recognition.
Weiss, Y., Torralba, A., & Fergus, R. (2009). Spectral hashing. In Advances in neural information processing systems.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jégou, H., Douze, M. & Schmid, C. Improving Bag-of-Features for Large Scale Image Search. Int J Comput Vis 87, 316–336 (2010). https://doi.org/10.1007/s11263-009-0285-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-009-0285-2