Abstract
Vector of locally aggregated descriptors (VLAD) is a promising approach for addressing the problem of image search on a very large scale. This representation is proposed to overcome the quantization error problem faced in Bag-of-Words (BoW) representation. However, text search engines have not be used yet for indexing VLAD given that it is not a sparse vector of occurrence counts. For this reason BoW approach is still the most widely adopted method for finding images that represent the same object or location given an image as a query and a large set of images as dataset.
In this paper, we propose to enable inverted files of standard text search engines to exploit VLAD representation to deal with large-scale image search scenarios. We show that the use of inverted files with VLAD significantly outperforms BoW in terms of efficiency and effectiveness on the same hardware and software infrastructure.
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References
Amato, G., Bolettieri, P., Falchi, F., Gennaro, C., Rabitti, F.: Combining local and global visual feature similarity using a text search engine. In: 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 49–54 (June 2011)
Amato, G., Falchi, F., Gennaro, C.: On reducing the number of visualwords in the bag-of-features representation. In: Battiato, S., Braz, J. (eds.) VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications, Barcelona, Spain, February 21-24, vol. 1, pp. 657–662. SciTePress (2013) ISBN: 978-989-8565-47-1
Amato, G., Gennaro, C., Savino, P.: Mi-file: using inverted files for scalable approximate similarity search. In: Multimedia Tools and Applications, pp. 1–30 (2012)
Amato, G., Savino, P.: Approximate similarity search in metric spaces using inverted files. In: Proceedings of the 3rd International Conference on Scalable Information Systems, InfoScale 2008, pp. 28:1–28:10. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels (2008)
Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval - the concepts and technology behind search, 2nd edn. Pearson Education Ltd., Harlow (2011)
Chávez, E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. Pattern Anal. Mach. Intell. 30(9), 1647–1658 (2008)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, SCG 2004, pp. 253–262. ACM, New York (2004)
Esuli, A.: Mipai: Using the pp-index to build an efficient and scalable similarity search system. In: Proceedings of the 2009 Second International Workshop on Similarity Search and Applications, SISAP 2009, pp. 146–148. IEEE Computer Society, Washington, DC (2009)
Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)
Gennaro, C., Amato, G., Bolettieri, P., Savino, P.: An approach to content-based image retrieval based on the lucene search engine library. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 55–66. Springer, Heidelberg (2010)
Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems 11, pp. 487–493. MIT Press (1998)
Jégou, H., Douze, M., Sánchez, J., Pérez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304–3311 (June 2010)
Jegou, H., Douze, M., Schmid, C.: Packing bag-of-features. In: 2009 IEEE 12th International Conference on Computer Vision, September 29 - October 2, pp. 2357–2364 (2009)
Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 3304–3311 (June 2010)
Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating local image descriptors into compact codes. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (September 2012) QUAERO
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (June 2007)
Perronnin, F., Liu, Y., Sanchez, J., Poirier, H.: Large-scale image retrieval with compressed fisher vectors. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3384–3391 ( June 2010)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2007)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, ICCV 2003, vol. 2. IEEE Computer Society, Washington, DC (2003)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis. 3(3), 177–280 (2008)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search - The Metric Space Approach. Advances in Database Systems, vol. 32. Kluwer (2006)
Zhang, X., Li, Z., Zhang, L., Ma, W.-Y., Shum, H.-Y.: Efficient indexing for large scale visual search. In: 2009 IEEE 12th International Conference on Computer Vision, September 29-October 2, vol. 2, pp. 1103–1110 (2009)
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Amato, G., Bolettieri, P., Falchi, F., Gennaro, C. (2013). Large Scale Image Retrieval Using Vector of Locally Aggregated Descriptors. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds) Similarity Search and Applications. SISAP 2013. Lecture Notes in Computer Science, vol 8199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41062-8_25
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DOI: https://doi.org/10.1007/978-3-642-41062-8_25
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