Combining Fisher Vectors in Image Retrieval Using Different Sampling Techniques
Tomás Mardones, Héctor Allende, Claudio Moraga
2015
Abstract
This paper addresses the problem of content-based image retrieval in a large-scale setting. Most works in the area sample image patches using an affine invariant detector or in a dense fashion, but we show that both sampling methods are complementary. By using Fisher Vectors we show how several sampling methods can be combined in a simple fashion inquiring only in a small fixed computational cost while significantly increasing the precision of the image retrieval system. As a second contribution, we show Fisher Vectors using their variance component, normally ignored in image retrieval applications, have better performance than their mean component under certain relevant settings. Experiments with up to 1 million images indicate that the proposed method remains valid in large-scale image search.
References
- Arandjelovic, R. (2012). Three things everyone should know to improve object retrieval. In Proc. CVPR, pages 2911-2918.
- Arandjelovic, R. and Zisserman, A. (2013). VLAD. In Proc. CVPR, pages 1578-1585.
- All about Delhumeau, J., Gosselin, P.-H., Jégou, H., and Pérez, P. (2013). Revisiting the VLAD image representation. In Proc. ACM Int. Conf. on Multimedia, pages 653- 656.
- Douze, M., Ramisa, A., and Schmid, C. (2011). Combining attributes and Fisher vectors for efficient image retrieval. In Proc. CVPR, pages 745-752.
- Gong, Y., Lazebnik, S., Gordo, A., and Perronnin, F. (2013). Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. Pattern Analysis and Machine Intelligence, 35(12):2916-2929.
- Gordo, A., Rodriguez-Serrano, J. A., Perronnin, F., and Valveny, E. (2012). Leveraging category-level labels for instance-level image retrieval. In Proc. CVPR, pages 3045-3052.
- Huiskes, M. J. and Lew, M. S. (2008). The MIR Flickr retrieval evaluation. In Proc. ACM Int. Conf. on Multimedia Information Retrieval, pages 39-43.
- Jaakkola, T. S. and Haussler, D. (1999). Exploiting generative models in discriminative classifiers. In Proc. Conf. on Advances in Neural Information Processing Systems II, pages 487-493.
- Jégou, H. and Chum, O. (2012). Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening. In Proc. ECCV, pages 774-787.
- Jégou, H., Douze, M., and Schmid, C. (2008). Hamming embedding and weak geometric consistency for large scale image search. In Proc. ECCV, volume I, pages 304-317.
- Jégou, H., Douze, M., and Schmid, C. (2011). Product quantization for nearest neighbor search. Pattern Analysis and Machine Intelligence, 33(1):117-128.
- Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., and Schmid, C. (2012). Aggregating local image descriptors into compact codes. Pattern Analysis and Machine Intelligence, pages 1704-1716.
- Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
- Manning, C. D., Raghavan, P., and Schtze, H. (2008). Introduction to Information Retrieval. Cambridge University Press, New York.
- Nister, D. and Stewenius, H. (2006). Scalable recognition with a vocabulary tree. In Proc. CVPR, pages 2161- 2168.
- Perronnin, F. and Dance, C. R. (2007). Fisher kernels on visual vocabularies for image categorization. In Proc. CVPR, pages 1-8.
- Perronnin, F., Liu, Y., Snchez, J., and Poirier, H. (2010a). Large-scale image retrieval with compressed Fisher vectors. In Proc. CVPR, pages 3384-3391.
- Perronnin, F., Sánchez, J., and Mensink, T. (2010b). Improving the Fisher kernel for large-scale image classification. In Proc. ECCV, pages 143-156.
- Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In Proc. CVPR, pages 1-8.
- Sánchez, J., Perronnin, F., Mensink, T., and Verbeek, J. (2013). Image classification with the Fisher vector: Theory and practice. International Journal of Computer Vision, 105(3):222-245.
- Shahbaz Khan, F., Anwer, R., van de Weijer, J., Bagdanov, A., Vanrell, M., and Lopez, A. (2012). Color attributes for object detection. In Proc. CVPR, pages 3306- 3313.
- Tuytelaars, T. and Mikolajczyk, K. (2008). Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3):177-280.
- Wengert, C., Douze, M., and Jégou, H. (2011). Bag-ofcolors for improved image search. In ACM Multimedia, pages 1437-1440.
- Zhang, S., Yang, M., Cour, T., Yu, K., and Metaxas, D. (2012). Query specific fusion for image retrieval. In Proc. ECCV, pages 660-673.
- Zheng, L., Wang, S., Zhou, W., and Tian, Q. (2014). Bayes merging of multiple vocabularies for scalable image retrieval. In Proc. CVPR, pages 1963-1970.
Paper Citation
in Harvard Style
Mardones T., Allende H. and Moraga C. (2015). Combining Fisher Vectors in Image Retrieval Using Different Sampling Techniques . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 128-135. DOI: 10.5220/0005179201280135
in Bibtex Style
@conference{icpram15,
author={Tomás Mardones and Héctor Allende and Claudio Moraga},
title={Combining Fisher Vectors in Image Retrieval Using Different Sampling Techniques},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={128-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005179201280135},
isbn={978-989-758-077-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Combining Fisher Vectors in Image Retrieval Using Different Sampling Techniques
SN - 978-989-758-077-2
AU - Mardones T.
AU - Allende H.
AU - Moraga C.
PY - 2015
SP - 128
EP - 135
DO - 10.5220/0005179201280135