Abstract
The most popular approach to large scale image retrieval is based on the bag-of-word (BoW) representation of images. There is an important trick how the statistical and geometry features of BoW are efficiently used. We present a two-step approach for image retrieval with statistical features and spatial geometry information been considered in different step. In the first step, the statistical features of the images’ BoW are achieved to capture the underlying image topic to screen those images. In the second step, images from same topic are ranked using the concept of co-occurrence features (a type of geometry features). Computational cost of the retrieval is reduced because the first step does not consider computing the expensive spatial geometry information and the second step only uses significant features to rank images. Experiments on the Oxford 5K benchmark show that the proposed technique can stably achieve nearly the same result compared with a state-of-the-art retrieval method [1] while only spending about a tenth of the time the method takes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, Y.M., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 809–816 (2011)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: International Conference on Computer Vision, pp. 1470–1477 (2003)
Cao, Y., Wang, C., Li, Z., Zhang, L.: Spatial bag-of-features. In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp. 3352–3329 (2010)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2161–2168 (2006)
Philbin, J., Chum, O., Isard, M., et al.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Philbin, J., Chum, O., Isard, M., et al.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)
Philbin, J., Chum, O., Isard, M., et al.: Total recall: Automatic query expansion with a generative feature model for object retrieval. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Jegou, H., Douze, M., Schmid, C., Perez, P.: Aggregating local descriptors in a compact image representation. In: CVPR, San Francisco, USA, pp. 3304–3311 (2010)
Zhang, X., Li, Z., Zhang, L., Ma, W., Shum, H.Y.: Efficient indexing for large scale visual search. In: ICCV, Kyoto, Japan, pp. 1103–1110 (2009)
Jegou, H., Douze, M., Schmid, C., Perez, P.: Packing bag-of-features. In: ICCV, Kyoto, Japan, pp. 2357–2364 (2009)
Perdoch, M., Chum, O., Matas, J.: Efficient representation of local geometry for large scale object retrieval. In: CVPR, Miami, FL, USA, pp. 9–16 (2009)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)
Weiss, Y., Torralba, A., Reigus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 2169–2178 (2008)
Wang, F.Y., Zhang, S.W., Li, H.P., et al.: Image retrieval using multiple orders of Geometry-preserving Visual Phrases. In: International Conference on Image Analysis and Signal Processing, pp. 1–5 (2012)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Intemational Journal of Computer Vision 2, 91–110 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, Y., Feng, L., Wang, X., Guo, N. (2014). Image Retrieval Based on Statistical and Geometry Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_69
Download citation
DOI: https://doi.org/10.1007/978-3-319-14364-4_69
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
eBook Packages: Computer ScienceComputer Science (R0)